Seismic, petrological and geodynamical constraints on thermal and compositional structure of the upper mantle: global thermochemical models

DEFF Research Database (Denmark)

Cammarano, Fabio; Tackley, Paul J.; Boschi, Lapo

2011-01-01

Mapping the thermal and compositional structure of the upper mantle requires a combined interpretation of geophysical and petrological observations. Based on current knowledge of material properties, we interpret available global seismic models for temperature assuming end-member compositional...... structures. In particular, we test the effects of modelling a depleted lithosphere, which accounts for petrological constraints on continents. Differences between seismicmodels translate into large temperature and density variations, respectively, up to 400K and 0.06 g cm-3 at 150 km depth. Introducing...... lateral compositional variations does not change significantly the thermal interpretation of seismic models, but gives a more realistic density structure. Modelling a petrological lithosphere gives cratonic temperatures at 150 km depth that are only 100 K hotter than those obtained assuming pyrolite...

Preliminary petrological and geochemical results from the Salton Sea Geothermal Field, California: A near-field natural analog of a radioactive waste repository in salt: Topical report No. 2

International Nuclear Information System (INIS)

Elders, W.A.; Cohen, L.H.; Williams, A.E.; Neville, S.; Collier, P.; Oakes, C.

1986-03-01

High concentrations of radionuclides and high temperatures are not naturally encountered in salt beds. For this reason, the Salton Sea Geothermal Field (SSGF) may be the best available geologic analog of some of the processes expected to occur in high level nuclear waste repositories in salt. Subsurface temperatures and brine concentrations in the SSGF span most of the temperature range and fluid inclusion brine range expected in a salt repository, and the clay-rich sedimentary rocks are similar to those which host bedded or domal salts. As many of the chemical processes observed in the SSGF are similar to those expected to occur in or near a salt repository, data derived from it can be used in the validation of geochemical models of the near-field of a repository in salt. This report describes preliminary data on petrology and geochemistry, emphasizing the distribution of rare earth elements and U and Th, of cores and cuttings from several deep wells chosen to span a range of temperature gradients and salinities. Subsurface temperature logs have been augmented by fluid inclusion studies, to reveal the effects of brines of varying temperature and salinity. The presence of brines with different oxygen isotopic signatures also indicate lack of mixing. Whole rock major, minor and trace element analyses and data on brine compositions are being used to study chemical migration in these sediments. 65 refs., 20 figs., 3 tabs

Apollo 12 feldspathic basalts 12031, 12038, and 12072; petrology, comparison and interpretations

International Nuclear Information System (INIS)

Beaty, E.W.; Hill, S.M.R.; Albee, A.L.; Baldridge, W.S.

1979-01-01

Modal and chemical data indicate that 12072, 12038, and 12031, the Apollo 12 feldspathic basalts, form a well-defined group which cannot be related to the other Apollo 12 rock types. 12072 contains phenocrysts of olivine and pigeonite and microphenocrysts of Cr-spinel set in a fine-grained, variolitic groundmass. 12038 is a medium-grained, equigranular basalt with a texture indicating it was multiply saturated. 12031 is a coarse-grained rock with granular to graphic intergrowths of pyroxene and plagioclase; it was also multiply saturated. Petrologic observations, as well as the bulk chemistry, are consistent with the interpretation that 12031 could be derived from 12072 through fractionation of Cr-spinel, olivine, and pigeonite, the observed phenocryst assemblage. 12038, however, contains more pigeonite, less olivine, three times as much Ca-phosphate minerals, one-fifth as much troilite, and much more sodic plagioclase than 12072. These differences indicate that 12038 must have come from a separate igneous body. Consideration of the bulk compositions indicates that neither 12072 and 12031 nor 12038 could have been derived from the Apollo 12 olivine, pigeonite, or ilmenite basalts by crystal--liquid fractionation. The general petrologic similarities between 12072, 12031, and the other Apollo 12 basalts suggests that they were produced in either the same or similar source regions. 12038, however, is petrologically and chemically unique, and is probably exotic to the Apollo 12 landing site

The Run-up to Volcanic Eruption Unveiled by Forensic Petrology and Geophysical Observations

Science.gov (United States)

Rasmussen, D. J.; Plank, T. A.; Roman, D. C.

2017-12-01

Volcanoes often warn of impending eruptions. However, one of the greatest challenges in volcano research is translating precursory geophysical signals into physical magmatic processes. Petrology offers powerful tools to study eruption run-up that benefit from direct response to magmatic forcings. Developing these tools, and tying them to geophysical observations, will help us identify eruption triggers (e.g., magmatic recharge, gas build-up, tectonic events) and understand the significance of monitored signals of unrest. We present an overview of petrologic tools used for studying eruption run-up, highlighting results from our study of the 1999 eruption of Shishaldin volcano. Olivine crystals contain chemical gradients, the consequence of diffusion following magma mixing events, which is modeled to determine mixing timescales. Modeled timescales provide strong evidence for at least three mixing events, which were triggered by magmatic recharge. Petrologic barometers indicate these events occurred at very shallow depths (within the volcanic edifice). The first mixing event occurred nine months before eruption, which was signaled by a swarm of deep-long period earthquake. Minor recharge events followed over two months, which are indicated by a second deep-long period earthquake swarm and a change in the local stress orientation measured by shear-wave splitting. Following these events, the system was relatively quiet until a large mixing event occurred 45 days prior to eruption, which was heralded by a large earthquake (M5.2). Following this event, geophysical signals of unrest intensified and became continuous. The final mixing event, beginning roughly a week before eruption, represents the final perturbation to the system before eruption. Our findings point to a relatively long run-up, which was subtle at first and intensified several weeks before eruption. This study highlights the strong link between geophysical signals of volcanic unrest and magmatic events, and

Assessment of fire-damaged concrete. Combining metamorphic petrology and concrete petrography

NARCIS (Netherlands)

Larbi, J.A.; Nijland, T.G.

2001-01-01

Metamorphic petrology is a branch of geology that deals with the study of changes in rocks due changing physio-chemical conditions. As conditions shift in or out of the thermodynamic stability field of phases, new phases may appear whereas others disappear. A basic approach is mapping of so-called

Petrological features of selected components of the Cergowa sandstones (Outer Carpathians) recorded by scanning electron microscopy - preliminary study

Science.gov (United States)

Pszonka, Joanna

2017-11-01

The scanning electron microscope analysis of the Cergowa sandstones brings new data on their petrological features and chemical composition. Previous work in standard petrographic examination, e.g. polarising (PL) or cathodoluminescence (CL) microscopy, displayed limited information on grain surface topography and only assumptions to their geochemistry. Both identification and characterisation of minerals are fundamental in the progress of mining and minerals processing systems. Detrital grains of the Cergowa sandstones are bound by calcite and dolomitic cement and commonly corroded by diagenetic fluids, however, in varying degrees, which is illustrated here by feldspar, quartz and dolomite minerals. Dissolution processes of marginal parts of these mineral grains resulted in corrosion, which increased the contact surface between the grains and the cement. The difference in resistance to these processes was observed not only among distinct groups of minerals, but also within the group of feldspars: between K-feldspars and minerals of plagioclase. That combination resulted in exceptionally strong cementation of the Cergowa sandstones, which is expressed by their high hardness and resistance to abrasion, freezing, and thawing. Inherent parameters of sandstones are characterised by their petrographical properties.

Sedimentary Petrology: from Sorby to the globalization of Sedimentary Geology

International Nuclear Information System (INIS)

Alonso-Zarza, A. M.

2013-01-01

We describe here the most important milestones and contributions to Sedimentary Petrology compared to other geological disciplines. We define the main aim of our study and the scientific and economic interests involved in Sedimentary Petrology. The body of the paper focuses upon the historical development of this discipline from Henry Sorby's initial work until the present day. The major milestones in its history include: 1) initial descriptive works; 2) experimental studies; 3) the establishment of the different classifications of sedimentary rocks; 4) studies into facies and sedimentary environments; 5) advances in the study of diagenetic processes and their role in hydrocarbon prospection; and 6) the development of Sedimentary Geochemistry. Relationships and coincidences with Sedimentology are discussed. We go on to look at the advances that have taken place over the last 30 years, in which the study of sedimentary rocks is necessarily included in the wider field of Sedimentary Geology as a logical result of the proposal of global models of a changing Earth in which Sedimentary Geology plays a significant part. Finally we mention the notable contributions of Spanish sedimentary petrologists to this whole field of science. (Author) 120 refs.

Petrography and petrology of the Hamadan pegmatites

International Nuclear Information System (INIS)

Valizadeh, M.V.; Torkian, A.

2000-01-01

Petrological investigation on the pegmatites of Hamadan area was carried out for their abundance, mineralogical variations and their distribution. They reveal the genesis of Granitoid of Alvand in western parts of Iran in Sanandaj - Sirjan metamorphic belt. Field investigations show that pegmatites are mainly dispersed both on north and south of Alvand mass. They mainly consist of Graphic - pegmatites, Tourmaline Pegmatites, Aluminosilicate - pegmatites and Quartz veins. Muscovite - Aluminosilicate pegmatites are located only in south and outside of granitoid mass, for example near Dehnow Asad - Ol - llah - Khan and Manga villages. Regarding to field investigation, mineralogical characteristics and based on radiometric dating the age of biotites of granitoid is a bout 70-80 M.Y. and the age of Muscovite - pegmatites is about 100 M.Y. Therefore, pegmatites are prior to Alvand emplacement. This is in accordance with pegmatites genesis idea proposed by Winkler and von Platen. So, we suppose that pegmatites of Alvand are metamorphic and their formation do not follow normal magmatic trends. Our petrologic investigation shows that as a result of movement of Arabic plate towards Iranian pa lte (SW - NE), sedimentary rocks composing of metamorphed clays (meta-sediments) in 680-800 d eg C and 2-5 kbar was melted resulting in aplitic melt to come upwards. With the present of thermal dome, transportation of water and mineralizing gas large crystals of Muscovite and Tourmaline were formed slowly and gradually pegmatites were formed. In this condition a melt from sandstone-shale source began to move upward and in different T-P condition it formed aluminosilicate pegmatites. Each of these assemblages present specific conditions of formation

Teaching Igneous and Metamorphic Petrology Through Guided Inquiry Projects

Science.gov (United States)

McMillan, N. J.

2003-12-01

Undergraduate Petrology at New Mexico State University (GEOL 399) has been taught using three, 5-6 week long projects in place of lectures, lab, and exams for the last six years. Reasons for changing from the traditional format include: 1) to move the focus from identification and memorization to petrologic thinking; 2) the need for undergraduate students to apply basic chemical, structural, and field concepts to igneous and metamorphic rocks; 3) student boredom in the traditional mode by the topic that has captivated my professional life, in spite of my best efforts to offer thrilling lectures, problems, and labs. The course has three guided inquiry projects: volcanic, plutonic, and pelitic dynamothermal. Two of the rock suites are investigated during field trips. Each project provides hand samples and thin sections; the igneous projects also include whole-rock major and trace element data. Students write a scientific paper that classifies and describes the rocks, describes the data (mineralogical and geochemical), and uses data to interpret parameters such as tectonic setting, igneous processes, relationship to phase diagrams, geologic history, metamorphic grade, metamorphic facies, and polymetamorphic history. Students use the text as a major resource for self-learning; mini-lectures on pertinent topics are presented when needed by the majority of students. Project scores include evaluation of small parts of the paper due each Friday and participation in peer review as well as the final report. I have found that petrology is much more fun, although more difficult, to teach using this method. It is challenging to be totally prepared for class because students are working at different speeds on different levels on different aspects of the project. Students enjoy the course, especially the opportunity to engage in scientific investigation and debate. A significant flaw in this course is that students see fewer rocks and have less experience in rock classification

Petrology, mineralogy and geochemistry of surficial uranium deposits

International Nuclear Information System (INIS)

Pagel, M.

1984-01-01

A comprehensive understanding of the petrology, mineralogy, and geochemistry of surficial uranium ore deposits is important for developing prospecting and evaluation strategies. Carnotite is the main uranium mineral and is found in those deposits that have the greatest potential uranium resources. The following uranium-bearing minerals have been reported to occur in surficial deposits: carnotite, tyuyamunite, soddyite, weeksite, haiweeite, uranophane, betauranophane, metaankoleite, torbernite, autunite, phosphuranylite, schroeckingerite, Pb-V-U hydroxide (unnamed mineral), uraninite and organourano complexes. The interrelationships between some of the minerals of the host rocks (especially the clays) are not well understood. (author)

Geochemistry and petrology of mafic Proterozoic and Permian dykes on Bornholm, Denmark:

DEFF Research Database (Denmark)

Holm, Paul Martin; Pedersen, Lise E.; Højsteeen, Birte

2010-01-01

More than 250 dykes cut the mid Proterozoic basement gneisses and granites of Bornholm. Most trend between NNW and NNE, whereas a few trend NE and NW. Field, geochemical and petrological evidence suggest that the dyke intrusions occurred as four distinct events at around 1326 Ma (Kelseaa dyke...

Mineralogy, petrology and geochemistry of carbonaceous chondritic clasts in the LEW 85300 polymict eucrite

Science.gov (United States)

Zolensky, M. E.; Hewins, R. H.; Mittlefehldt, D. W.; Lindstrom, M. M.; Xiao, X.; Lipschutz, M. E.

1992-01-01

We have performed a detailed petrologic and mineralogic study of two chondritic clasts from the polymict eucrite Lewis Cliff (LEW) 85300, and performed chemical analyses by INAA and RNAA on one of these. Petrologically, the clasts are identified and are composed of dispersed aggregates, chondrules, and chondrule fragments supported by matrix. The aggregates and chondrules are composed of olivine, orthopyroxene, plus some diopside. The matrix consists of fine-grained olivine, and lesser orthopyroxene and augite. Fine-grained saponite is common in the matrix. The bulk major composition of the clast studied by INAA and RNAA shows unusual abundance patterns for lithophile, siderophile and chalcophile elements but is basically chondritic. The INAA/RNAA data preclude assignment of the LEW 85300,15 clast to any commonly accepted group of carbonaceous chondrite.

Petrological features of selected components of the Cergowa sandstones (Outer Carpathians recorded by scanning electron microscopy – preliminary study

Directory of Open Access Journals (Sweden)

Pszonka Joanna

2017-01-01

Full Text Available The scanning electron microscope analysis of the Cergowa sandstones brings new data on their petrological features and chemical composition. Previous work in standard petrographic examination, e.g. polarising (PL or cathodoluminescence (CL microscopy, displayed limited information on grain surface topography and only assumptions to their geochemistry. Both identification and characterisation of minerals are fundamental in the progress of mining and minerals processing systems. Detrital grains of the Cergowa sandstones are bound by calcite and dolomitic cement and commonly corroded by diagenetic fluids, however, in varying degrees, which is illustrated here by feldspar, quartz and dolomite minerals. Dissolution processes of marginal parts of these mineral grains resulted in corrosion, which increased the contact surface between the grains and the cement. The difference in resistance to these processes was observed not only among distinct groups of minerals, but also within the group of feldspars: between K–feldspars and minerals of plagioclase. That combination resulted in exceptionally strong cementation of the Cergowa sandstones, which is expressed by their high hardness and resistance to abrasion, freezing, and thawing. Inherent parameters of sandstones are characterised by their petrographical properties.

Petrology of seamounts in the Central Indian Ocean Basin: Evidence for near-axis origin

Digital Repository Service at National Institute of Oceanography (India)

Mukhopadhyay, R.; Batiza, R.; Iyer, S.D.

Previous studies on the distribution and morphology of ancient seamount chains (>50 Ma) in the Central Indian Ocean basin (CIOB) indicated their generation from the fast spreading Southeast Indian Ridge. The petrology of some of these seamounts...

Mineralogical, petrological and geochemical aspects of alkaline and alkaline-carbonatite associations from Brazil

Science.gov (United States)

Morbidelli, L.; Gomes, C. B.; Beccaluva, L.; Brotzu, P.; Conte, A. M.; Ruberti, E.; Traversa, G.

1995-12-01

A general description of Mesozoic and Tertiary (Fortaleza) Brazilian alkaline and alkaline-carbonatite districts is presented with reference to mineralogy, petrology, geochemistry and geochronology. It mainly refers to scientific results obtained during the last decade by an Italo-Brazilian research team. Alkaline occurrences are distributed across Brazilian territory from the southern (Piratini, Rio Grande do Sul State) to the northeastern (Fortaleza, Ceará State) regions and are mainly concentrated along the borders of the Paraná Basin generally coinciding with important tectonic lineaments. The most noteworthy characteristics of these alkaline and alkaline-carbonatite suites are: (i) prevalence of intrusive forms; (ii) abundance of cumulate assemblages (minor dunites, frequent clinopyroxenites and members of the ijolite series) and (iii) abundance of evolved rock-types. Many data demonstrate that crystal fractionation was the main process responsible for magma evolution of all Brazilian alkaline rocks. A hypothesis is proposed for the genesis of carbonatite liquids by immiscibility processes. The incidence of REE and trace elements for different major groups of lithotypes, belonging both to carbonatite-bearing and carbonatite-free districts, are documented. Sr and preliminary Nd isotopic data are indicative of a mantle origin for the least evolved magmas of all the studied occurrences. Mantle source material and melting models for the generation of the Brazilian alkaline magma types are also discussed.

Semantically Enabling Knowledge Representation of Metamorphic Petrology Data

Science.gov (United States)

West, P.; Fox, P. A.; Spear, F. S.; Adali, S.; Nguyen, C.; Hallett, B. W.; Horkley, L. K.

2012-12-01

More and more metamorphic petrology data is being collected around the world, and is now being organized together into different virtual data portals by means of virtual organizations. For example, there is the virtual data portal Petrological Database (PetDB, http://www.petdb.org) of the Ocean Floor that is organizing scientific information about geochemical data of ocean floor igneous and metamorphic rocks; and also The Metamorphic Petrology Database (MetPetDB, http://metpetdb.rpi.edu) that is being created by a global community of metamorphic petrologists in collaboration with software engineers and data managers at Rensselaer Polytechnic Institute. The current focus is to provide the ability for scientists and researchers to register their data and search the databases for information regarding sample collections. What we present here is the next step in evolution of the MetPetDB portal, utilizing semantically enabled features such as discovery, data casting, faceted search, knowledge representation, and linked data as well as organizing information about the community and collaboration within the virtual community itself. We take the information that is currently represented in a relational database and make it available through web services, SPARQL endpoints, semantic and triple-stores where inferencing is enabled. We will be leveraging research that has taken place in virtual observatories, such as the Virtual Solar Terrestrial Observatory (VSTO) and the Biological and Chemical Oceanography Data Management Office (BCO-DMO); vocabulary work done in various communities such as Observations and Measurements (ISO 19156), FOAF (Friend of a Friend), Bibo (Bibliography Ontology), and domain specific ontologies; enabling provenance traces of samples and subsamples using the different provenance ontologies; and providing the much needed linking of data from the various research organizations into a common, collaborative virtual observatory. In addition to better

A Magnetic Petrology Database for Satellite Magnetic Anomaly Interpretations

Science.gov (United States)

Nazarova, K.; Wasilewski, P.; Didenko, A.; Genshaft, Y.; Pashkevich, I.

2002-05-01

A Magnetic Petrology Database (MPDB) is now being compiled at NASA/Goddard Space Flight Center in cooperation with Russian and Ukrainian Institutions. The purpose of this database is to provide the geomagnetic community with a comprehensive and user-friendly method of accessing magnetic petrology data via Internet for more realistic interpretation of satellite magnetic anomalies. Magnetic Petrology Data had been accumulated in NASA/Goddard Space Flight Center, United Institute of Physics of the Earth (Russia) and Institute of Geophysics (Ukraine) over several decades and now consists of many thousands of records of data in our archives. The MPDB was, and continues to be in big demand especially since recent launching in near Earth orbit of the mini-constellation of three satellites - Oersted (in 1999), Champ (in 2000), and SAC-C (in 2000) which will provide lithospheric magnetic maps with better spatial and amplitude resolution (about 1 nT). The MPDB is focused on lower crustal and upper mantle rocks and will include data on mantle xenoliths, serpentinized ultramafic rocks, granulites, iron quartzites and rocks from Archean-Proterozoic metamorphic sequences from all around the world. A substantial amount of data is coming from the area of unique Kursk Magnetic Anomaly and Kola Deep Borehole (which recovered 12 km of continental crust). A prototype MPDB can be found on the Geodynamics Branch web server of Goddard Space Flight Center at http://core2.gsfc.nasa.gov/terr_mag/magnpetr.html. The MPDB employs a searchable relational design and consists of 7 interrelated tables. The schema of database is shown at http://core2.gsfc.nasa.gov/terr_mag/doc.html. MySQL database server was utilized to implement MPDB. The SQL (Structured Query Language) is used to query the database. To present the results of queries on WEB and for WEB programming we utilized PHP scripting language and CGI scripts. The prototype MPDB is designed to search database by major satellite magnetic

Upper Cretaceous to Pleistocene melilitic volcanic rocks of the Bohemian Massif: Petrology and mineral chemistry

Czech Academy of Sciences Publication Activity Database

Skála, Roman; Ulrych, Jaromír; Krmíček, Lukáš; Fediuk, F.; Balogh, K.; Hegner, E.

2015-01-01

Roč. 66, č. 3 (2015), s. 197-216 ISSN 1335-0552 Institutional support: RVO:67985831 Keywords : Bohemian Massif * Cenozoic volcanism * isotope geochemistry * melilitic rock * mineralogy * petrology Subject RIV: DB - Geology ; Mineralogy Impact factor: 1.523, year: 2015

Investigating combined influence of petrology and technological parameters on strength of porous coke body

Energy Technology Data Exchange (ETDEWEB)

Dinel' t, V.M.; Shkoller, M.B.; Stankevich, A.S.; Korchuganova, G.S.

1983-09-01

The VUKhIN branch in Kuznetsk investigated effects of coal petrology and coking conditions on structural strength of coke in blast furnaces. Structural strength of coke produced from black coal from the Kuzbass as well as structural strength of coke partially gasified by carbon dioxide under conditions similar to those in blast furnaces was investigated. Fourteen samples of coal mixtures from the Kuzbass were used. Regression analysis was applied. Equations for forecasting coke properties on the basis of coal petrology and selected parameters characterizing coking were derived. Analyses showed that coke structural strength was decisively influenced by coefficients which characterized the average reflectivity of vitrinite in a coal mixture and its average density. After partial coke gasification by carbon dioxide effects of coefficients which characterized coal mixture nonhomogeneity (fluctuations of vitrinite reflectivity) and coal mixture density increased. Increasing coal density partially compensated negative effects of fluctuations of vitrinite reflectivity on coke structural strength. (10 refs.) (In Russian)

Mineralogy, petrology and whole-rock chemistry data compilation for selected samples of Yucca Mountain tuffs

International Nuclear Information System (INIS)

Connolly, J.R.

1991-12-01

Petrologic, bulk chemical, and mineralogic data are presented for 49 samples of tuffaceous rocks from core holes USW G-1 and UE-25a number-sign 1 at Yucca Mountain, Nevada. Included, in descending stratigraphic order, are 11 samples from the Topopah Spring Member of the Paintbrush Tuff, 12 samples from the Tuffaceous Beds of Calico Hills, 3 samples from the Prow Pass Member of the Crater Flat Tuff, 20 samples from the Bullfrog Member of the Crater Flat Tuff and 3 samples from the Tram Member of the Crater Flat Tuff. The suite of samples contains a wide variety of petrologic types, including zeolitized, glassy, and devitrified tuffs. Data vary considerably between groups of samples, and include thin section descriptions (some with modal analyses for which uncertainties are estimated), electron microprobe analyses of mineral phases and matrix, mineral identifications by X-ray diffraction, and major element analyses with uncertainty estimates

Assessment of statistical education in Indonesia: Preliminary results and initiation to simulation-based inference

Science.gov (United States)

Saputra, K. V. I.; Cahyadi, L.; Sembiring, U. A.

2018-01-01

Start in this paper, we assess our traditional elementary statistics education and also we introduce elementary statistics with simulation-based inference. To assess our statistical class, we adapt the well-known CAOS (Comprehensive Assessment of Outcomes in Statistics) test that serves as an external measure to assess the student’s basic statistical literacy. This test generally represents as an accepted measure of statistical literacy. We also introduce a new teaching method on elementary statistics class. Different from the traditional elementary statistics course, we will introduce a simulation-based inference method to conduct hypothesis testing. From the literature, it has shown that this new teaching method works very well in increasing student’s understanding of statistics.

Weathering of Igneous, Metamorphic, and Sedimentary Rocks in a Semi-arid Climate - An Engineering Application of Petrology

Science.gov (United States)

Harrison, W. J.; Wendlandt, R. F.

2003-12-01

Over the last 10 years, analytical methods have been introduced to students in CSM's undergraduate geological engineering program through a multi-year and multi-course approach. Beginning with principles and simple applications of XRD and SEM in sophomore Mineralogy and building on these skills in subsequent junior and senior year courses, geological engineers acquire proficiency in analytical methods. Essential workplace skills are thus acquired without adding an extra course in the undergraduate program. The following exercise is completed by juniors in an integrated Ig.-Met.-Sed. petrology course. The identification of clay mineral assemblages in soils provides a unique opportunity to demonstrate how basic principles of petrology and geochemistry are applied to engineering design criteria in construction site preparation. Specifically, the problem investigates the conditions leading to the formation of smectite in soils and the resulting construction risk due to soil expansion. Students examine soils developed on igneous, metamorphic, and sedimentary rocks near Denver, Colorado. The field locations are areas of suburban growth and several have expansive soil problems. The 2-week exercise includes sample collection, description, and preparation, determining clay mineralogy by XRD, and measurement of Atterberg Plasticity Indices. Teaching materials may be found at: http://serc.carleton.edu/NAGTWorkshops/petrology03/. This exercise accomplishes three objectives: First, skills in XRD analysis are developed by introducing students to concepts of particle size separation, particle orientation, and sequential analysis steps which are standard practices in clay characterization. Second, lecture material on the geochemistry of weathering of different rock types is reinforced. Students interpret the origin of clay mineral assemblages developed in soils derived from Precambrian gneisses, lower Paleozoic feldspathic sandstones, upper Paleozoic marine shales, and Tertiary

Petrology of lunar rocks and implication to lunar evolution

Science.gov (United States)

Ridley, W. I.

1976-01-01

Recent advances in lunar petrology, based on studies of lunar rock samples available through the Apollo program, are reviewed. Samples of bedrock from both maria and terra have been collected where micrometeorite impact penetrated the regolith and brought bedrock to the surface, but no in situ cores have been taken. Lunar petrogenesis and lunar thermal history supported by studies of the rock sample are discussed and a tentative evolutionary scenario is constructed. Mare basalts, terra assemblages of breccias, soils, rocks, and regolith are subjected to elemental analysis, mineralogical analysis, trace content analysis, with studies of texture, ages and isotopic composition. Probable sources of mare basalts are indicated.

Petrology and radiogeology of the Stripa pluton

International Nuclear Information System (INIS)

Wollenberg, Harold; Flexser, Steve; Andersson, Lennart

1980-01-01

To better define the character of the rock encompassing the thermomechanical and hydrological experiments at the Stripa mine in central Sweden, and to help determine the size of the Stripa pluton, detailed studies were conducted of the petrology and radiogeology of the quartz monzonite and adjacent rocks. Petrologic studies emphasized optical petrography, with supplementary X-ray diffraction, X-ray fluorescence and microprobe analyses. Radiogeologic investigations were based primarily on surface and underground gamma-ray spectrometric measurements of uranium, thorium and potassium, supplemented by laboratory gamma spectrometric analyses and fission-track radiographic determinations of the locations and abundance of uranium in the rock matrix. Both the quartz monzonite and the metavolcanic leptite which it intruded are strongly fractured. Two stages of fracture filling are evident; an earlier stage encompassing quartz, sericite, feldspar, epidote, and chlorite, and a later stage dominated by carbonate minerals. The Stripa quartz monzonite is chemically and mineralogically distinct from other plutons in the region. Muscovite is the predominant mica in the quartz monzonite; biotite has been altered to chlorite, hornblende is absent, and accessory minerals are scarce. In contrast, in other plutons in the Stripa region biotite and hornblende are prominent mafic minerals and accessory minerals are abundant. The Stripa quartz monzonite is also considerably more radioactive than the leptite and other plutons in the region. Uranium and thorium abundances are both- 30 ppm, considerably higher than in 'normal' granitic rocks where the thorium-to-uranium ratio generally exceeds 2. Potassium-argon dating of muscovite from the Stripa quartz monzonite indicates that this rock may be older, at 1691 million years than granitic rock of the neighboring Gusselby and Kloten massifs, whose ages, based on K-Ar dating of biotite, are respectively 1604 and 1640 m.y. Heat flow and heat

Petrology and radiogeology of the Stripa pluton

Energy Technology Data Exchange (ETDEWEB)

Wollenberg, Harold; Flexser, Steve; Andersson, Lennart

1980-12-01

To better define the character of the rock encompassing the thermomechanical and hydrological experiments at the Stripa mine in central Sweden, and to help determine the size of the Stripa pluton, detailed studies were conducted of the petrology and radiogeology of the quartz monzonite and adjacent rocks. Petrologic studies emphasized optical petrography, with supplementary X-ray diffraction, X-ray fluorescence and microprobe analyses. Radiogeologic investigations were based primarily on surface and underground gamma-ray spectrometric measurements of uranium, thorium and potassium, supplemented by laboratory gamma spectrometric analyses and fission-track radiographic determinations of the locations and abundance of uranium in the rock matrix. Both the quartz monzonite and the metavolcanic leptite which it intruded are strongly fractured. Two stages of fracture filling are evident; an earlier stage encompassing quartz, sericite, feldspar, epidote, and chlorite, and a later stage dominated by carbonate minerals. The Stripa quartz monzonite is chemically and mineralogically distinct from other plutons in the region. Muscovite is the predominant mica in the quartz monzonite; biotite has been altered to chlorite, hornblende is absent, and accessory minerals are scarce. In contrast, in other plutons in the Stripa region biotite and hornblende are prominent mafic minerals and accessory minerals are abundant. The Stripa quartz monzonite is also considerably more radioactive than the leptite and other plutons in the region. Uranium and thorium abundances are both- 30 ppm, considerably higher than in "normal" granitic rocks where the thorium-to-uranium ratio generally exceeds 2. Potassium-argon dating of muscovite from the Stripa quartz monzonite indicates that this rock may be older, at 1691 million years than granitic rock of the neighboring Gusselby and Kloten massifs, whose ages, based on K-Ar dating of biotite, are respectively 1604 and 1640 m.y. Heat flow and heat

INAA and petrological study of sandstones from the Angkor monuments

International Nuclear Information System (INIS)

Kucera, J.; Kranda, K.; Soukal, L.; Novak, J.K.; Lang, M.; Poncar, J.; Krausova, I.; Cunin, O.

2008-01-01

We determined 35 major, minor and trace elements in sandstone samples taken from building blocks of 19 Angkor temples and from an old and a new quarry using INAA. We also characterized the sandstone samples with conventional microscopy and electron microprobe analysis. Using cluster analysis, we found no straightforward correlation between the chemical/petrological properties of the sandstones and a presumed period of individual temples construction. The poor correlation may result either from the inherent inhomogeneity of sandstone or just reflect the diversity of quarries that supplied building blocks for the construction of any particular temple. (author)

Geology and petrology of alkaline Massif from Ilha de Vitoria, Sao Paulo State

International Nuclear Information System (INIS)

Motoki, A.

1986-01-01

Geological and petrological studies of the Vitoria Island Alkaline Complex, State of Sao Paulo, have been carried out by means of photo interpretation; field work, thin section studies, whole-rock chemical analysis, x-ray diffractometry, EPMA mineral analysis, and K-Ar and Rb-Sr dating. Radiometric dating indicates a late Cretaceous age for the Vitoria Island Alkaline Complex, which is concordant with the ages of other neighbouring alkaline bodies. (author)

Using a Differential Scanning Calorimeter to Teach Phase Equilibria to Students of Igneous and Metamorphic Petrology

Science.gov (United States)

Maria, Anton H.; Millam, Evan L.; Wright, Carrie L.

2011-01-01

As an aid for teaching phase equilibria to undergraduate students of igneous and metamorphic petrology, we have designed a laboratory exercise that allows them to create a phase diagram from data produced by differential scanning calorimetry. By preparing and analyzing samples of naphthalene and phenanthrene, students acquire hands-on insight into…

Petrology and oxygen isotope geochemistry of the Pucon ignimbrite - Southern Andean volcanic zone, Chile: Implications for genesis of mafic ignimbrites

International Nuclear Information System (INIS)

McCurry, Michael; Schmidt, Keegan

2001-01-01

Although mafic components of dominantly intermediate to silicic ignimbrites are rather common, voluminous, dominantly mafic ignimbrites are rare (e.g., Smith, 1979; cf. Freundt and Schmincke, 1995). Volcan Villarrica, the most active composite volcano in South America, located in the Southern Andean Volcanic Zone (SAVZ, Lopez-Escobar and Moreno, 1994a), has produced two such ignimbrites, respectively the Lican and Pucon Ignimbrites, in the last 14,000 years (Clavero, 1996). The two ignimbrites are low-Si andesite and basaltic-andesite to low-Si andesite, respectively, the former about twice as voluminous as the later (10 and 5 km 3 ). Eruption of the ignimbrites produced calderas respectively 5 and 2 km in diameter (Moreno, 1995; Clavero, 1996). In addition to its mafic bulk composition, the Pucon Ignimbrite (PI) is also distinguished by numerous xenolithic fragments among and also within magmatic pyroclasts. Many of these are fragments of granitoid rocks. Volcan Villarrica has also produced numerous smaller mafic ignimbrites and pyroclastic surge deposits, as well as dominantly basaltic fallout and lava flows (Lopez-Escobar and Moreno, 1994; Moreno, 1995; Clavero, 1996; Hickey-Vargas et al., 1989; Tormey et al., 1991). Reasons for the unusual style of mafic explosive activity at Volcan Villarrica are unclear. Clavero (1996), based upon an exemplary thesis-study of the physical volcanology and petrology of the PI, suggests it formed in response to a sequence of events beginning with injection of a shallow basaltic andesite magma chamber by hotter basaltic magma. In his model mixing and heat transfer between the two magmas initiated a violent Strombolian eruption that destabilized the chamber causing infiltration of large amounts of meteoric-water saturated country rocks. The Pucon Ignimbrite formed in response to subsequent phreatomagmatic interactions. In contrast, Lopez-Escobar and Moreno (1994) infer on geochemical grounds that volatiles leading to the explosive

Mineralogy, Petrology, Chronology, and Exposure History of the Chelyabinsk Meteorite and Parent Body

Science.gov (United States)

Righter, K.; Abell, P.; Agresti, D.; Berger, E. L.; Burton, A. S.; Delaney, J. S.; Fries, M. D.; Gibson, E. K.; Harrington, R.; Herzog, G. F.;

Preliminary Test of Adaptive Neuro-Fuzzy Inference System Controller for Spacecraft Attitude Control

Directory of Open Access Journals (Sweden)

Sung-Woo Kim

2012-12-01

Full Text Available The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS. An ANFIS produces a control signal for one of the three axes of a spacecraft’s body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

Petrology, isotopic and fluid inclusion studies of eclogites from Sujiahe, NW Dabie Shan (China), July 1 2002

NARCIS (Netherlands)

Fu, B.; Zheng, Y.-F.; Touret, J.L.R.

2002-01-01

In addition to the Triassic Hong'an low-T-high-P eclogite and the Xinxian coesite-bearing kyanite-glaucophane eclogite, Silurian coesite-free amphibole eclogites occur in the Sujiahe region, NW Dabie Shan of central China. A comprehensive study of petrology, Nd-Sr, O-H isotopes and fluid inclusions

The Private Lives of Minerals: Social Network Analysis Applied to Mineralogy and Petrology

Science.gov (United States)

Hazen, R. M.; Morrison, S. M.; Fox, P. A.; Golden, J. J.; Downs, R. T.; Eleish, A.; Prabhu, A.; Li, C.; Liu, C.

2016-12-01

Comprehensive databases of mineral species (rruff.info/ima) and their geographic localities and co-existing mineral assemblages (mindat.org) reveal patterns of mineral association and distribution that mimic social networks, as commonly applied to such varied topics as social media interactions, the spread of disease, terrorism networks, and research collaborations. Applying social network analysis (SNA) to common assemblages of rock-forming igneous and regional metamorphic mineral species, we find patterns of cohesion, segregation, density, and cliques that are similar to those of human social networks. These patterns highlight classic trends in lithologic evolution and are illustrated with sociograms, in which mineral species are the "nodes" and co-existing species form "links." Filters based on chemistry, age, structural group, and other parameters highlight visually both familiar and new aspects of mineralogy and petrology. We quantify sociograms with SNA metrics, including connectivity (based on the frequency of co-occurrence of mineral pairs), homophily (the extent to which co-existing mineral species share compositional and other characteristics), network closure (based on the degree of network interconnectivity), and segmentation (as revealed by isolated "cliques" of mineral species). Exploitation of large and growing mineral data resources with SNA offers promising avenues for discovering previously hidden trends in mineral diversity-distribution systematics, as well as providing new pedagogical approaches to teaching mineralogy and petrology.

Petrology and organic geochemistry of the lower Miocene lacustrine sediments (Most Basin, Eger Graben, Czech Republic)

Czech Academy of Sciences Publication Activity Database

Havelcová, Martina; Sýkorová, Ivana; Mach, K.; Trejtnarová, Hana; Blažek, Jaroslav

2015-01-01

Roč. 139, Special issue (2015), s. 26-39 ISSN 0166-5162 R&D Projects: GA ČR(CZ) GA13-18482S Institutional support: RVO:67985891 Keywords : Most Basin * Miocene * coal facies indices * coal petrology * organic geochemistry Subject RIV: DD - Geochemistry Impact factor: 3.294, year: 2015 http://www.sciencedirect.com/science/article/pii/S0166516214001529#

The lithospheric structure beneath Ireland and surrounding areas from integrated geophysical-petrological modelling of magnetic and other geophysical data

Science.gov (United States)

Baykiev, E.; Guerri, M.; Fullea, J.

2017-12-01

The availability of unprecedented resolution aeromagnetic data in Ireland (Tellus project, http://www.tellus.ie/) in conjunction with new satellite magnetic data (e.g., ESÁs Swarm mission) has opened the possibility of detailed modelling of the Irish subsurface magnetic structure. A detailed knowledge of the magnetic characteristics (susceptibility, magnetite content) of the crust is relevant for a number of purposes, including geological mapping and mineral and geothermal energy prospection. In this work we model the magnetic structure of Ireland and surrounding areas using primarily aeromagnetic and satellite observations but also other geophysical data sets. To this aim we use a geophysical-petrological modelling tool (LitMod) in which key properties of rocks (i.e., density, electrical conductivity and seismic velocities) that can be inferred from geophysical data (gravity, seismic, EM) are self consistently determined based on the thermochemical conditions (using the software Perple_X). In contrast to the mantle, where thermodynamic equilibrium is prevalent, in the crust metastable conditions are dominant, i.e. rock properties may not be representative of the current, in situ, temperature and pressure conditions. Instead, the rock properties inferred from geophysical data may be reflecting the mineralogy stable at rock formation conditions. In addition, temperature plays a major role in the distribution of the long wavelength crustal magnetic anomalies. Magnetite retains its magnetic properties below its Curie temperature (585 ºC) and the depth of Curie's isotherm provides an estimate of the thickness of the magnetic crust. Hence, a precise knowledge of the crustal geotherm is required to consistently model crustal magnetic anomalies. In this work LitMod has been modified to account for metastable crustal lithology, to predict susceptibility in the areas below Curie's temperature, and to compute magnetic anomalies based on a magnetic tesseroid approach. The

Mineralogy, petrology and geochemistry of the Pocos de Caldas analogue study sites, Minas Gerais, Brazil

International Nuclear Information System (INIS)

Waber, M.

1991-01-01

The thorium-rare-earth element deposit at Morro do Ferro is of supergene origin and was formed under lateritic weathering conditions. The ore body forms shallow NW-SE elongated argillaceous lenses that extend from the top of the hill downwards along its south-eastern slope. The deposit is capped by a stockwork of magnetite veins which have protected the underlying, highly argillaceous host rock from excessive erosion. The surrounding country rocks comprise a sequence of subvolcanic phonolite intrusions that have been strongly altered by hydrothermal and supergene processes. From petrological, mineralogical and geochemical studies and mass balance calculations, it is inferred that the highly weathered host rock was originally carbonatic in composition and was initially enhanced in thorium and rare-earth elements compared to the surrounding silicate rocks. Intrusion of the carbonatite produced fenitic alteration of the surrounding phonolites, consisting of an early potassic alteration followed by a vein-type Th-REE mineralization with associated fluorite, carbonate, pyrite and zircon. Subsequent lateritic weathering has completely destroyed the carbonatite, forming a residual supergene enrichment of Th and REEs. Initial weathering of the carbonatite leading to solutions enriched in carbonate and phosphate may have appreciably restricted the dissolution of the primary Th-REE phases. Strongly oxidic weathering has resulted in a fractionation between cerium and the other light rare-earth elements. Ce 3+ is oxidized to Ce 4+ and retained together with thorium by secondary mineral formation and adsorption on poorly crystalline iron- and aluminium-hydroxides. In contrast, the trivalent LREEs are retained to a lesser degree and are thus more available for secondary mineral formation and adsorption at greater depths down the weathering column. (author) figs., tabs., 60 refs

Research on petrologic, geochemical characteristics and genesis of volcanic rocks in Dachangsha basin

International Nuclear Information System (INIS)

Wei Sanyuan

1999-01-01

On the basis of research on petrologic, geochemical characteristics and isotope composition of volcanic rocks in Dachangsha basin, the author concludes that the volcanic rocks formed from magma of different genesis and depth are double-cycle effusive. It is proposed that the magma forming the intermediate-basic volcanics of the first cycle comes from the mixing of the partial melting of the deep crust and mantle, and the intermediate-acidic volcanics of the secondary cycle are derived from the remelting of the upper crust

Entropic Inference

Science.gov (United States)

Caticha, Ariel

2011-03-01

In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.

Petrology and geochemistry of the Miocene-Pliocene fluvial succession, Katawaz Basin, Western Pakistan: Implications on provenance and source area weathering

DEFF Research Database (Denmark)

Kasi, Aimal K.; Kassi, Aktar Muhammad; Friis, Henrik

Petrology and geochemistry of sandstones and mudstones of the Miocene Dasht Murgha Group (DMG) and Pliocene Malthanai Formation (MF) of the Pishin Belt (Katawaz Basin), northwestern Pakistan have been carried out to find out their provenance and source area weathering. Sandstones of the Dasht...

Garnet Signatures in Geophysical and Geochemical Observations: Insights into the Thermo-Petrological Structure of Oceanic Upper Mantle

Science.gov (United States)

Grose, C. J.; Afonso, J. C.

2013-12-01

We have developed new physically comprehensive thermal plate models of the oceanic lithosphere which incorporate temperature- and pressure-dependent heat transport properties and thermal expansivity, melting beneath ridges, hydrothermal circulation near ridge axes, and insulating oceanic crust. These models provide good fits to global databases of seafloor topography and heat flow, and seismic evidence of thermal structure near ridge axes. We couple these thermal plate models with thermodynamic models to predict the petrology of oceanic lithosphere. Geoid height predictions from our models suggest that there is a strong anomaly in geoid slope (over age) above ~25 Ma lithosphere due to the topography of garnet-field mantle. A similar anomaly is also present in geoid data over fracture zones. In addition, we show that a new assessment of a large database of ocean island basalt Sm/Yb systematics indicates that there is an unmistakable step-like increase in Sm/Yb values around 15-20 Ma, indicating the presence of garnet. To explain this feature, we have attempted to couple our thermo-petrological models of oceanic upper mantle with an open system, non-modal, dynamic melting model with diffusion kinetics to investigate trace element partitioning in an ascending mantle column.

Petrology and Geochemistry of Unbrecciated Harzburgitic Diogenite MIL 07001: A Window Into Vestan Geological Evolution

Science.gov (United States)

Mittlefehldt, D. W.; Peng, Z. X.; Mertzman, S. A.; Mertzman, K. R.

2014-01-01

There is a strong case that asteroid 4 Vesta is the parent of the howardite, eucrite and diogenite (HED) meteorites. Models developed for the geological evolution of Vesta can satisfy the compositions of basaltic eucrites that dominate in the upper crust. The bulk compositional characteristics of diogenites - cumulate harzburgites and orthopyroxenites from the lower crust - do not fit into global magma ocean models that can describe the compositions of basaltic and cumulate eucrites. Recent more detailed formation models do make provision for a more complicated origin for diogenites, but this model has yet to be completely vetted. Compositional studies of bulk samples has led to the hypothesis that many diogenites were formed late by interaction of their parent melts with a eucritic crust, but those observations may alternatively be explained by subsolidus equilibration of trace elements between orthopyroxene and plagioclase and Ca-phosphate in the rocks. Differences in radiogenic Mg-26 content between diogenites and eucrites favors early formation of the former, not later formation. Understanding the origin of diogenites is crucial for understanding the petrologic evolution of Vesta. We have been doing coordinated studies of a suite of diogenites including petrologic investigations, bulk rock major and trace element studies, and in situ trace element analyses of orthopyroxene. Here we will focus on an especially unusual, and potentially key, diogenite, MIL 07001.

More than one kind of inference: re-examining what's learned in feature inference and classification.

Science.gov (United States)

Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

Perceptual inference.

Science.gov (United States)

Aggelopoulos, Nikolaos C

2015-08-01

Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience. Copyright © 2015 Elsevier Ltd. All rights reserved.

SEMANTIC PATCH INFERENCE

DEFF Research Database (Denmark)

Andersen, Jesper

2009-01-01

Collateral evolution the problem of updating several library-using programs in response to API changes in the used library. In this dissertation we address the issue of understanding collateral evolutions by automatically inferring a high-level specification of the changes evident in a given set ...... specifications inferred by spdiff in Linux are shown. We find that the inferred specifications concisely capture the actual collateral evolution performed in the examples....

Magmatic plumbing system of Kilauea Volcano: Insights from Petrologic and Geochemical Monitoring

Science.gov (United States)

Garcia, M. O.; Pietruszka, A. J.; Marske, J.; Greene, A.; Lynn, K. J.

2016-12-01

Monitoring the petrology and geochemistry of lavas from active volcanoes in near realtime affords the opportunity to formulate and evaluate models for magma transport, mixing, and storage to help predict eruption scenarios with greater confidence and better understand magmatic plumbing systems (e.g., Poland et al. 2012, Nat. Geosci. 5, 295-300). Continous petrologic and geochemical monitoring of two ongoing eruptions at the summit and east rift zone of Kilauea Volcano on the Island of Hawaii have revealed much about the dynamics of magmatic processes. When the composition of lava shifted to a more MgO-rich composition in April 1983, we predicted that the Puu Oo eruption would not be short-lived. We had no idea it would continue for over 33 years. Subsequent changes in lava composition have highlighted the interplay between mixing pockets of rift-zone stored magma with new mantle-derived magma and the cooling-induced crystal fractionation during brief (usually days) eruption hiatuses. Surprisingly, the mantle derived magma has continued to change in composition including several 10-year cycles in Pb isotope ratios superimposed on a progressive depletion in highly incompatible elements (Greene et al. 2013, G3, doi: 10.1002/ggge.20285). These compositional trends are contrary to those observed for sustained basaltic eruptions on continents and argue for melt extraction from a multi-component source with 1-3 km wide heterogeneities. Compositional zoning within olivine phenocrysts, created by diffusive re-equilibration, also provide insights into magma mixing, storage, and transport at Kilauea. Timescales modeling of Fe-Mg and Ni concentration gradients within Puu Oo olivine indicate that crystals can be stored at magmatic temperatures for months to a few years before eruption (Shea et al. 2015, Geology 43, 935-938). Kilauea's ongoing eruptions continue to provide a dynamic laboratory for positing and testing models for the generation and evolution of basaltic magma.

Multimodel inference and adaptive management

Science.gov (United States)

Rehme, S.E.; Powell, L.A.; Allen, Craig R.

2011-01-01

Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.

Petrology of Oligocene Ghaleh Yaghmesh granitoids in the west of Yazd province

Directory of Open Access Journals (Sweden)

Bahareh Fazeli

2017-02-01

that magma mixing process was likely responsible for the formation of the rocks being studied. Acknowledgements The authors would like to thank the University of Isfahan for the financial support. We also thank the Southern Methodist University (SMU (Dallas - USA for the XRF chemical analysis undertaken for this project. References Alavi, M., 1994. Tectonics of Zagros orogenic belt of Iran, new data and interpretation. Tectonophysics, 229(3: 211–238. Chappell, B.W. and White, A.J., 1974. Two contrasting granite types. Pacific Geology, 8: 173-174. Clemens, J.D., Stevens G., and Farina, F., 2011. The enigmatic sources of I-type granites: The peritectic conexión. Lithos, 126(3: 174–181. Didier, J., 1991. The main types of enclaves in the Hercynian granitoids of the Massif Central, France. In: J. Didier and B. Barbarin (Editors, Enclaves and Granite Petrology. Developments in Petrology, V. 13. Elsevier, Amsterdam, pp. 47–61. Didier, J. and Barbarin, B., 1991. Enclaves and granite petrology.Developments in Petrology, V. 13. Elsevier, Amsterdam, 625 pp. Ellis, D.J. and Thompson, A.B., 1986. Subsolidus and partial melting reactions in the quartz-excess and water deficient conditions of peraluminous melts from mafic rocks. Journal of Petrology, 27(1: 91-121. Honarmand, M., Rashidnejad-Omran, N., Corfu , F., Emami, M. H. and Nabatian, G., 2013. Geochronology and magmatic history of a calc-alkaline plutonic complex in the Urumieh-Dokhtar Magmatic Belt, Central Iran: Zircon ages as evidence for two major plutonic episodes. Neues Jahrbuch fur Mineralogie, Abhandlungen, 190(1: 67–77. Kananian, A., Sarjoughian, F., Nadimi A., Ahmadian, J. and Ling, W., 2014. Geochemical characteristics of the Kuh-e Dom intrusion, Urumieh–Dokhtar Magmatic Arc (Iran: Implications for source regions and magmatic evolution. Journal of Asian Earth Sciences, 90: 137-148. Sepahi, A.A. and Malvandi, F., 2008. Petrology of the Bouein Zahra-Naein Plutonic Complexes, Urumieh-Dokhtar Belt, Iran: With

The role of amphibole in Merapi arc magma petrogenesis: insights from petrology and geochemistry of lava hosted xenoliths and xenocrysts

Science.gov (United States)

Chadwick, J. P.; Troll, V. R.; Schulz, B.; Dallai, L.; Freda, C.; Schwarzkopf, L. M.; Annersten, H.; Skogby, H.

2010-05-01

Recently, increasing attention has been paid to the role of amphibole in the differentiation of arc magmas. The geochemical composition of these magmas suggests that deep to mid crustal fractionation of amphibole has occurred. However, this phase is typically an infrequent modal phenocryst phase in subduction zone eruptive deposits(1). Nevertheless, erupted material only represents a portion of the magmatism produced in subduction zone settings, with many opportunities for melts to stall on route to the surface. This discrepancy between whole rock geochemistry and petrological interpretation of arc magmas has lead many scientists to postulate that, at mid to deep crustal levels, there may be significant volumes of amphibole bearing lithologies. Amphibole instability at shallow levels can also contribute to its scarcity in eruptive deposits. This argument is strengthened by field and petrological evidence, including the widespread occurrence of amphibole-rich intrusive rocks in exhumed orogenicbelts formed during subduction zone activity, e.g. the Adamello batholith (2),as well as the presence of amphibole-rich xenoliths and xenocrysts preserved in arc lavas worldwide, e.g. in Indonesia, Antilles, and Central America. Thus, amphibole appears to play an integral role in subduction zone magmatism and identifying and constraining this role is central to understanding arc magma petrogenisis. Amphibole-rich melts or bodies in the deep to mid crust could be a significant hydrous reservoir for intra-crustal melts and fluids (1). In this preliminary study, we have carried out petrological and geochemical analyses of recent basaltic andesite and amphibole bearing crystalline igneous inclusions and xenocrysts from Merapi volcano in Java, Indonesia. The basaltic andesite geochemistry is consistent with amphibole fractionation and the crystalline inclusions are cogenetic to the Merapi magmatic system. These inclusions are likely to represent fractionation residues reflecting

Optimal inference with suboptimal models: Addiction and active Bayesian inference

Science.gov (United States)

Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl

2015-01-01

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321

Teaching Mineralogy, Petrology and Geochemistry in the 21st Century: Instructional Resources for Geoscience Faculty

Science.gov (United States)

Mogk, D. W.; Beane, R. J.; Whitney, D. L.; Nicolaysen, K. E.; Panero, W. R.; Peck, W. H.

2011-12-01

Mineralogy, petrology and geochemistry (MPG) are pillars of the geoscience curriculum because of their relevance in interpreting Earth history and processes, application to geo-hazards, resources, and environmental issues, and contributions to emerging fields such as geology and human health. To keep faculty current in scientific advances in these fields, and in modern instructional methods, the On the Cutting Edge program convened a workshop at the University of Minnesota in August, 2011. This workshop builds on the previous 15 year's work that has been focused on identifying, aggregating, and developing high-quality collections of teaching activities and related resources, and in building a community of scholars in support of excellence in instruction in MPG courses. The goals of the workshop were to: a) develop an integrated, comprehensive and reviewed curriculum for MPG courses, and to seek ways to make connections with the larger geoscience curriculum; b) to explore emerging topics in MPG such as geobiology and climate change; c) demonstrate effective methods in teaching MPG in the context of Earth system science; d) share effective teaching activities and strategies for the classroom, laboratory and field including advances in pedagogy, assessments and research on learning; e) keep faculty current on recent advances in mineralogy, petrology and geochemistry research and to apply these findings to our teaching; f) explore and utilize current societal and global issues that intersect mineralogy, petrology and geochemistry to heighten the relevancy of course content for students; and h) meet colleagues and foster future teaching and research collaborations. A significant outcome of this workshop is a peer reviewed of collection of 300+ existing teaching activities, and a gap analysis to identify teaching activities needed to make these collections comprehensive and coherent. In addition, a series of thematic collections were developed to assist high priority

Geochemical and petrological considerations about the basalts of upper aluminium in the Fildes Peninsula. (Rei George), Antartica

International Nuclear Information System (INIS)

Machado, A.; Fernandes de Lima, E.; Chemale, F.

1998-01-01

Petrographic, geochemical and petrological studies of lower Tertiary basaltic rocks from Fildes Peninsula in Antarctica were made to characterize their source and magmatic evolution. These basaltic rocks have porphyritic, glomeroporphyritic, intergranular and intersertal textures. The phenocrysts are of plagioclase (An), augite, pigeonite and Ti-magnetite. These basaltic rocks have AL O from 16 to 22%, Ni from 6 to 88 ppm, Co from 24 to 33 ppm and Cr from 54 to 123 ppm. Enrichment of Rb. Ba, Sr and LREE with respect to HREE is observed as relative depleted in HFSE is detected. The mass balance realized to understand the evolution of liquid that gave source the different basaltic rocks. Showed that the extracted mineral fractions were 76% of plagioclase, 2% of clinopiroxene and 21% of olivine. The intermediate volcanic rocks of Fildes Peninsula can be explained by cristalization fractionation of a basic liquid. The isotopic dates showed initial rations of Sr/Sr <0,704 and positive values of Nd epsilon. These results are strong support a mantelic source for basaltic rocks of Fildes Peninsula. On basis of geochemical, petrological and isotopic characteristics is possible concluded that these rocks were formed in an island are environment with parcial melting of mantle wedge. (author)

Inference rule and problem solving

Energy Technology Data Exchange (ETDEWEB)

Goto, S

1982-04-01

Intelligent information processing signifies an opportunity of having man's intellectual activity executed on the computer, in which inference, in place of ordinary calculation, is used as the basic operational mechanism for such an information processing. Many inference rules are derived from syllogisms in formal logic. The problem of programming this inference function is referred to as a problem solving. Although logically inference and problem-solving are in close relation, the calculation ability of current computers is on a low level for inferring. For clarifying the relation between inference and computers, nonmonotonic logic has been considered. The paper deals with the above topics. 16 references.

Knowledge and inference

CERN Document Server

Nagao, Makoto

1990-01-01

Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of """"knowledge"""" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intellig

Geometric statistical inference

International Nuclear Information System (INIS)

Periwal, Vipul

1999-01-01

A reparametrization-covariant formulation of the inverse problem of probability is explicitly solved for finite sample sizes. The inferred distribution is explicitly continuous for finite sample size. A geometric solution of the statistical inference problem in higher dimensions is outlined

A crustal-upper mantle model for southeastern Sicily (Italy) from the integration of petrologic and geophysical data

Science.gov (United States)

Manuella, Fabio Carmelo; Brancato, Alfonso; Carbone, Serafina; Gresta, Stefano

2013-05-01

An interdisciplinary approach is proposed to investigate the structure and composition of the Permo-Triassic basement of the Hyblean Plateau and Sicily Channel. Comparisons of published data on peridotites and spinels from different geodynamic settings, and new data on Hyblean spinels, reveal the affinity of the Hyblean basement with an ultra-slow spreading oceanic lithosphere, rather than with the Africa continental plate. Similar results derive from volcanic rocks of the studied area, whose Nb/Yb vs. Th/Yb ratio hints at their affinity with the MORB-OIB array, even excluding any possible contamination with continental crust lithologies, unlike North Africa lavas. The comparison of He isotopic ratios from Hyblean Plateau and Sicily Channel highlights their similarity with values measured in fluids emitted from the Rainbow and Logatchev hydrothermal fields in Mid-Atlantic Ridge. Based on petrologic and geochemical evidence for the oceanic nature of the Permo-Triassic basement in southeastern Sicily, and the occurrence of serpentinized harzburgite xenoliths in Hyblean diatremes, the P-wave velocity model proposed for the investigated area is used to estimate lithospheric pressure, density, degree of serpentinization and magnetic susceptibility also considering both abyssal and ophiolitic serpentinites. The resulting values suggest the presence of peridotites affected by different degrees of serpentinization (35-100 vol.%) ranging to a depth of 8-19 km. As a whole, combined seismic, gravimetric and magnetic data indicate the presence of a marked anomaly at a depth of about 19 km. As a consequence, we consider the Moho discontinuity as a serpentinization front, by fixing the relative top at a depth of 19 km. Our results suggest that the oceanic lithospheric model for southeastern Sicily could be broadened to the Sicily Channel, which is possibly correlated to the adjacent Ionian oceanic basin, inferred as belonging to the Oman-Iraq-Levantine-Sicily seaway.

South-Tibetan partially molten batholiths: geophysical characterization and petrological assessment of their origin

Science.gov (United States)

Hetényi, G.; Pistone, M.; Nabelek, P. I.; Baumgartner, L. P.

2017-12-01

Zones of partial melt in the middle crust of Lhasa Block, Southern Tibet, have been geophysically observed as seismically reflective "bright spots" in the past 20 years. These batholiths bear important relevance for geodynamics as they serve as the principal observation at depth supporting channel-flow models in the Himalaya-Tibet orogen. Here we assess the spatial abundance of and partial melt volume fraction within these crustal batholiths, and establish lower and upper estimate bounds using a joint geophysical-petrological approach.Geophysical imaging constrains the abundance of partial melt zones to 5.6 km3 per surface-km2 on average (minimum: 3.1 km3/km2, maximum: 7.6 km3/km2 over the mapped area). Physical properties detected by field geophysics and interpreted by laboratory measurements constrain the amount of partial melt to be between 5 and 26 percent.We evaluate the compatibility of these estimates with petrological modeling based on geotherms, crustal bulk rock compositions and water contents consistent with the Lhasa Block. These simulations determine: (a) the physico-chemical conditions of melt generation at the base of the Tibetan crust and its transport and emplacement in the middle crust; (b) the melt percentage produced at the source, transported and emplaced to form the observed "bright spots". Two main mechanisms are considered: (1) melting induced by fluids produced during mineral dehydration reactions in the underthrusting Indian lower crust; (2) dehydration-melting reactions caused by heating within the Tibetan crust. We find that both mechanisms demonstrate first-order match in explaining the formation of the partially molten "bright spots". Thermal modelling shows that the Lhasa Block batholiths have only small amounts of melt and only for geologically short times (features of the geodynamic evolution. Their transience excludes both long-distance and long-lasting channel flow transport in Tibet.

Preliminary report on NTS spectral gamma logging and calibration models

International Nuclear Information System (INIS)

Mathews, M.A.; Warren, R.G.; Garcia, S.R.; Lavelle, M.J.

1985-01-01

Facilities are now available at the Nevada Test Site (NTS) in Building 2201 to calibrate spectral gamma logging equipment in environments of low radioactivity. Such environments are routinely encountered during logging of holes at the NTS. Four calibration models were delivered to Building 2201 in January 1985. Each model, or test pit, consists of a stone block with a 12-inch diameter cored borehole. Preliminary radioelement values from the core for the test pits range from 0.58 to 3.83% potassium (K), 0.48 to 29.11 ppm thorium (Th), and 0.62 to 40.42 ppm uranium (U). Two satellite holes, U19ab number2 and U19ab number3, were logged during the winter of 1984-1985. The response of these logs correlates with contents of the naturally radioactive elements K. Th. and U determined in samples from petrologic zones that occur within these holes. Based on these comparisons, the spectral gamma log aids in the recognition and mapping of subsurface stratigraphic units and alteration features associated with unusual concentration of these radioactive elements, such as clay-rich zones

Petrology and geochemistry of Late Proterozoic hornblende gabbros from southeast of Fariman, Khorasan Razavi province, Iran

Directory of Open Access Journals (Sweden)

Seyed Masoud Homam

2015-04-01

Full Text Available Introduction Hornblende-bearing gabbroic rocks are quite common in subduction-related magmatic suites and considered to represent magmatic differentiation process in arc magmas (Heliker, 1995; Hickey-Vargas et al., 1995; Mandal and Ray, 2012. The presence of hornblende as an important mineral phase in gabbroic rocks of subduction zone has been considered either as an early crystallizing mineral from water-bearing mafic magmas (Beard and Borgia 1989; Mandal and Ray, 2012 or as a product of reaction of early crystallized minerals (olivine, pyroxene and plagioclase and water-rich evolved melt/aqueous fluid (Costa et al., 2002; Mandal and Ray, 2012. The careful study of petrology and geochemistry of hornblende-bearing gabbroic rocks from Chahak area, of Neoproterozoic age, can provide important information about their petrogenesis. Because of the special characteristics of Chahak hornblende gabbros according to their age and their situation in the main structural units of Iran, their study can present critical keys for the knowledge of geological history of Iran specially central Iran zone. Material and Methods This study carried out in two parts including field and laboratory works. Sampling and structural studies were carried out during field work. Geological map for the study area was also prepared. 65 thin and polished thin sections for petrographical purpose were studied. Major oxides, rare earth elements and trace elements were analyzed for 4 samples (92P-1, 92P-3, B1and B6 from hornblende gabbros on the basis of 4AB1 method using ICP-MS of ACME Laboratory from Canada. In addition, major oxides of three hornblende gabbro samples (89P-62, 89P-59 and 89P-46 were used from Partovifar (Partovifar, 2012. Results and discussion Fariman metamorphic terrains, of Proterozoic age, consist of metamorphosed sedimentary and igneous (plutonic and volcanic rocks. Hornblende gabbros of the study area include plagioclase, hornblende, biotite pyroxene and

Goal inferences about robot behavior : goal inferences and human response behaviors

NARCIS (Netherlands)

Broers, H.A.T.; Ham, J.R.C.; Broeders, R.; De Silva, P.; Okada, M.

2014-01-01

This explorative research focused on the goal inferences human observers draw based on a robot's behavior, and the extent to which those inferences predict people's behavior in response to that robot. Results show that different robot behaviors cause different response behavior from people.

Introduction to statistical inference and its applications with R

CERN Document Server

Trosset, Michael W

2009-01-01

ExperimentsExamples Randomization The Importance of Probability Games of Chance Mathematical Preliminaries Sets Counting Functions Limits Probability Interpretations of Probability Axioms of Probability Finite Sample Spaces Conditional Probability Random VariablesCase Study: Padrolling in Milton Murayama's All I asking for is my bodyDiscrete Random VariablesBasic Concepts Examples Expectation Binomial DistributionsContinuous Random Variables A Motivating Example Basic Concepts Elementary Examples Normal Distributions Normal Sampling DistributionsQuantifying Population Attributes Symmetry Quantiles The Method of Least SquaresData The Plug-In Principle Plug-In Estimates of Mean and Variance Plug-In Estimates of Quantiles Kernel Density Estimates Case Study: Are Forearm Lengths Normally Distributed? TransformationsLots of Data Averaging Decreases Variation The Weak Law of Large Numbers The Central Limit TheoremInferenceA Motivating Example Point EstimationHeuristics of Hypothesis Testing Testing Hypotheses about...

Entropic Inference

OpenAIRE

Caticha, Ariel

2010-01-01

In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEn...

Learning Convex Inference of Marginals

OpenAIRE

Domke, Justin

2012-01-01

Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suffers difficulties when either the inference process or the model is approximate. In this paper, the inference process is first defined to be the minimization of a convex function, inspired by free energy approximations. Learning is then done directly in terms of the performance of the inference process at univariate marginal prediction. The main ...

A re-evaluation of palaeoclimatic conditions during the Pleistocene and Holocene from the western continental shelf of India - Evidences from the petrology of the limestones

Digital Repository Service at National Institute of Oceanography (India)

Rao, V.P.; Nair, R.R.

and 150 m water depth were studied for their petrology and to evaluate the palaeoclimatic conditions during Quaternary. The limestones characteristic of abundant microspar and pseudospar are found at water depths 65 and 95 m, respectively...

Exploring Chondrule and CAI Rims Using Micro- and Nano-Scale Petrological and Compositional Analysis

Science.gov (United States)

Cartwright, J. A.; Perez-Huerta, A.; Leitner, J.; Vollmer, C.

2017-12-01

As the major components within chondrites, chondrules (mm-sized droplets of quenched silicate melt) and calcium-aluminum-rich inclusions (CAI, refractory) represent the most abundant and the earliest materials that solidified from the solar nebula. However, the exact formation mechanisms of these clasts, and whether these processes are related, remains unconstrained, despite extensive petrological and compositional study. By taking advantage of recent advances in nano-scale tomographical techniques, we have undertaken a combined micro- and nano-scale study of CAI and chondrule rim morphologies, to investigate their formation mechanisms. The target lithologies for this research are Wark-Lovering rims (WLR), and fine-grained rims (FGR) around CAIs and chondrules respectively, present within many chondrites. The FGRs, which are up to 100 µm thick, are of particular interest as recent studies have identified presolar grains within them. These grains predate the formation of our Solar System, suggesting FGR formation under nebular conditions. By contrast, WLRs are 10-20 µm thick, made of different compositional layers, and likely formed by flash-heating shortly after CAI formation, thus recording nebular conditions. A detailed multi-scale study of these respective rims will enable us to better understand their formation histories and determine the potential for commonality between these two phases, despite reports of an observed formation age difference of up to 2-3 Myr. We are using a combination of complimentary techniques on our selected target areas: 1) Micro-scale characterization using standard microscopic and compositional techniques (SEM-EBSD, EMPA); 2) Nano-scale characterization of structures using transmission electron microscopy (TEM) and elemental, isotopic and tomographic analysis with NanoSIMS and atom probe tomography (APT). Preliminary nano-scale APT analysis of FGR morphologies within the Allende carbonaceous chondrite has successfully discerned

Probabilistic inductive inference: a survey

OpenAIRE

Ambainis, Andris

2001-01-01

Inductive inference is a recursion-theoretic theory of learning, first developed by E. M. Gold (1967). This paper surveys developments in probabilistic inductive inference. We mainly focus on finite inference of recursive functions, since this simple paradigm has produced the most interesting (and most complex) results.

LAIT: a local ancestry inference toolkit.

Science.gov (United States)

Hui, Daniel; Fang, Zhou; Lin, Jerome; Duan, Qing; Li, Yun; Hu, Ming; Chen, Wei

2017-09-06

Inferring local ancestry in individuals of mixed ancestry has many applications, most notably in identifying disease-susceptible loci that vary among different ethnic groups. Many software packages are available for inferring local ancestry in admixed individuals. However, most of these existing software packages require specific formatted input files and generate output files in various types, yielding practical inconvenience. We developed a tool set, Local Ancestry Inference Toolkit (LAIT), which can convert standardized files into software-specific input file formats as well as standardize and summarize inference results for four popular local ancestry inference software: HAPMIX, LAMP, LAMP-LD, and ELAI. We tested LAIT using both simulated and real data sets and demonstrated that LAIT provides convenience to run multiple local ancestry inference software. In addition, we evaluated the performance of local ancestry software among different supported software packages, mainly focusing on inference accuracy and computational resources used. We provided a toolkit to facilitate the use of local ancestry inference software, especially for users with limited bioinformatics background.

Bayesian statistical inference

Directory of Open Access Journals (Sweden)

Bruno De Finetti

2017-04-01

Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.

Is there a hierarchy of social inferences? The likelihood and speed of inferring intentionality, mind, and personality.

Science.gov (United States)

Malle, Bertram F; Holbrook, Jess

2012-04-01

People interpret behavior by making inferences about agents' intentionality, mind, and personality. Past research studied such inferences 1 at a time; in real life, people make these inferences simultaneously. The present studies therefore examined whether 4 major inferences (intentionality, desire, belief, and personality), elicited simultaneously in response to an observed behavior, might be ordered in a hierarchy of likelihood and speed. To achieve generalizability, the studies included a wide range of stimulus behaviors, presented them verbally and as dynamic videos, and assessed inferences both in a retrieval paradigm (measuring the likelihood and speed of accessing inferences immediately after they were made) and in an online processing paradigm (measuring the speed of forming inferences during behavior observation). Five studies provide evidence for a hierarchy of social inferences-from intentionality and desire to belief to personality-that is stable across verbal and visual presentations and that parallels the order found in developmental and primate research. (c) 2012 APA, all rights reserved.

INFERENCE BUILDING BLOCKS

Science.gov (United States)

2018-02-15

expressed a variety of inference techniques on discrete and continuous distributions: exact inference, importance sampling, Metropolis-Hastings (MH...without redoing any math or rewriting any code. And although our main goal is composable reuse, our performance is also good because we can use...control paths. • The Hakaru language can express mixtures of discrete and continuous distributions, but the current disintegration transformation

Practical Bayesian Inference

Science.gov (United States)

Bailer-Jones, Coryn A. L.

2017-04-01

Preface; 1. Probability basics; 2. Estimation and uncertainty; 3. Statistical models and inference; 4. Linear models, least squares, and maximum likelihood; 5. Parameter estimation: single parameter; 6. Parameter estimation: multiple parameters; 7. Approximating distributions; 8. Monte Carlo methods for inference; 9. Parameter estimation: Markov chain Monte Carlo; 10. Frequentist hypothesis testing; 11. Model comparison; 12. Dealing with more complicated problems; References; Index.

Logical inference and evaluation

International Nuclear Information System (INIS)

Perey, F.G.

1981-01-01

Most methodologies of evaluation currently used are based upon the theory of statistical inference. It is generally perceived that this theory is not capable of dealing satisfactorily with what are called systematic errors. Theories of logical inference should be capable of treating all of the information available, including that not involving frequency data. A theory of logical inference is presented as an extension of deductive logic via the concept of plausibility and the application of group theory. Some conclusions, based upon the application of this theory to evaluation of data, are also given

Progress in 1988 1990 with computer applications in the ``hard-rock'' arena: Geochemistry, mineralogy, petrology, and volcanology

Science.gov (United States)

Rock, Nicholas M. S.

This review covers rock, mineral and isotope geochemistry, mineralogy, igneous and metamorphic petrology, and volcanology. Crystallography, exploration geochemistry, and mineral exploration are excluded. Fairly extended comments on software availability, and on computerization of the publication process and of specimen collection indexes, may interest a wider audience. A proliferation of both published and commercial software in the past 3 years indicates increasing interest in what traditionally has been a rather reluctant sphere of geoscience computer activity. However, much of this software duplicates the same old functions (Harker and triangular plots, mineral recalculations, etc.). It usually is more efficient nowadays to use someone else's program, or to employ the command language in one of many general-purpose spreadsheet or statistical packages available, than to program a specialist operation from scratch in, say, FORTRAN. Greatest activity has been in mineralogy, where several journals specifically encourage publication of computer-related activities, and IMA and MSA Working Groups on microcomputers have been convened. In petrology and geochemistry, large national databases of rock and mineral analyses continue to multiply, whereas the international database IGBA grows slowly; some form of integration is necessary to make these disparate systems of lasting value to the global "hard-rock" community. Total merging or separate addressing via an intelligent "front-end" are both possibilities. In volcanology, the BBC's videodisk Volcanoes and the Smithsonian Institution's Global Volcanism Project use the most up-to-date computer technology in an exciting and innovative way, to promote public education.

Inference

DEFF Research Database (Denmark)

Møller, Jesper

(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.1 with the ......(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.......1 with the title ‘Inference'.) This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus...

Lower complexity bounds for lifted inference

DEFF Research Database (Denmark)

Jaeger, Manfred

2015-01-01

instances of the model. Numerous approaches for such “lifted inference” techniques have been proposed. While it has been demonstrated that these techniques will lead to significantly more efficient inference on some specific models, there are only very recent and still quite restricted results that show...... the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established earlier in Jaeger (2000; Jaeger, M. 2000. On the complexity of inference about...... that under the assumption that NETIME≠ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier-, and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference and for knowledge bases not containing...

Variations on Bayesian Prediction and Inference

Science.gov (United States)

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

Lunar Science Conference, 4th, Houston, Tex., March 5-8, 1973, Proceedings. Volume 1 - Mineralogy and petrology. Volume 2 - Chemical and isotope analyses. Organic chemistry. Volume 3 - Physical properties

Science.gov (United States)

Gose, W. A.

1973-01-01

The mineralogy, petrology, chemistry, isotopic composition, and physical properties of lunar materials are described in papers detailing methods, results, and implications of research on samples returned from eight lunar landing sites: Apollo 11, 12, 14, 15, 16, 17, and Luna 16 and 20. The results of experiments conducted or set up on the lunar surface by the astronauts are also described along with observations taken from Command Modules and subsatellites. Major topics include general geology, soil and breccia studies, petrologic studies, mineralogic analyses, elemental compositions, radiometric age determinations, rare gas chemistry, radionuclides, organogenic compounds, particle track records, thermal properties, seismic studies, resonance studies, orbital mapping, lunar atmosphere, magnetic studies, electrical studies, optical properties, and microcratering. Individual items are announced in this issue.

Adaptive Inference on General Graphical Models

OpenAIRE

Acar, Umut A.; Ihler, Alexander T.; Mettu, Ramgopal; Sumer, Ozgur

2012-01-01

Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional ...

The inference from a single case: moral versus scientific inferences in implementing new biotechnologies.

Science.gov (United States)

Hofmann, B

2008-06-01

Are there similarities between scientific and moral inference? This is the key question in this article. It takes as its point of departure an instance of one person's story in the media changing both Norwegian public opinion and a brand-new Norwegian law prohibiting the use of saviour siblings. The case appears to falsify existing norms and to establish new ones. The analysis of this case reveals similarities in the modes of inference in science and morals, inasmuch as (a) a single case functions as a counter-example to an existing rule; (b) there is a common presupposition of stability, similarity and order, which makes it possible to reason from a few cases to a general rule; and (c) this makes it possible to hold things together and retain order. In science, these modes of inference are referred to as falsification, induction and consistency. In morals, they have a variety of other names. Hence, even without abandoning the fact-value divide, there appear to be similarities between inference in science and inference in morals, which may encourage communication across the boundaries between "the two cultures" and which are relevant to medical humanities.

Introductory statistical inference

CERN Document Server

Mukhopadhyay, Nitis

2014-01-01

This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques.Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of dist

Sedimentary Petrology: from Sorby to the globalization of Sedimentary Geology; La Petrologia Sedimentaria: desde Sorby a la globalizacion de la Geologia Sedimentaria

Energy Technology Data Exchange (ETDEWEB)

Alonso-Zarza, A M

2013-02-01

We describe here the most important milestones and contributions to Sedimentary Petrology compared to other geological disciplines. We define the main aim of our study and the scientific and economic interests involved in Sedimentary Petrology. The body of the paper focuses upon the historical development of this discipline from Henry Sorby's initial work until the present day. The major milestones in its history include: 1) initial descriptive works; 2) experimental studies; 3) the establishment of the different classifications of sedimentary rocks; 4) studies into facies and sedimentary environments; 5) advances in the study of diagenetic processes and their role in hydrocarbon prospection; and 6) the development of Sedimentary Geochemistry. Relationships and coincidences with Sedimentology are discussed. We go on to look at the advances that have taken place over the last 30 years, in which the study of sedimentary rocks is necessarily included in the wider field of Sedimentary Geology as a logical result of the proposal of global models of a changing Earth in which Sedimentary Geology plays a significant part. Finally we mention the notable contributions of Spanish sedimentary petrologists to this whole field of science. (Author) 120 refs.

Sedimentary Petrology: from Sorby to the globalization of Sedimentary Geology; La Petrologia Sedimentaria: desde Sorby a la globalizacion de la Geologia Sedimentaria

Energy Technology Data Exchange (ETDEWEB)

Alonso-Zarza, A. M.

2013-02-01

We describe here the most important milestones and contributions to Sedimentary Petrology compared to other geological disciplines. We define the main aim of our study and the scientific and economic interests involved in Sedimentary Petrology. The body of the paper focuses upon the historical development of this discipline from Henry Sorby's initial work until the present day. The major milestones in its history include: 1) initial descriptive works; 2) experimental studies; 3) the establishment of the different classifications of sedimentary rocks; 4) studies into facies and sedimentary environments; 5) advances in the study of diagenetic processes and their role in hydrocarbon prospection; and 6) the development of Sedimentary Geochemistry. Relationships and coincidences with Sedimentology are discussed. We go on to look at the advances that have taken place over the last 30 years, in which the study of sedimentary rocks is necessarily included in the wider field of Sedimentary Geology as a logical result of the proposal of global models of a changing Earth in which Sedimentary Geology plays a significant part. Finally we mention the notable contributions of Spanish sedimentary petrologists to this whole field of science. (Author) 120 refs.

Active inference, communication and hermeneutics.

Science.gov (United States)

Friston, Karl J; Frith, Christopher D

2015-07-01

Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Petrology of Ortsog-Uul peridotite-gabbro massif in Western Mongolia

Science.gov (United States)

Shapovalova, M.; Tolstykh, N.; Shelepaev, R.; Cherdantseva, M.

2017-12-01

The Ortsog-Uul mafic-ultramafic massif of Western Mongolia is located in a tectonic block with overturned bedding. The massif hosts two intrusions: a rhythmically-layered peridotite-gabbro association (Intrusion 1) and massive Bt-bearing amphibole-olivine gabbro (Intrusion 2). Intrusions 1 and 2 have different petrology features. Early Intrusion 1 (278±2.5Ma) is characterized by lower concentrations of alkalis, titanium and phosphorus than late Intrusion 2 (272±2Ma). The chondrite-normalized REE and primitive mantle-normalized rare elements patterns of Ortsog-Uul intrusions have similar curves of elements distribution. However, Intrusion 2 is characterized higher contents of REE and rare elements. High concentrations of incompatible elements are indicative of strong fractionation process. It has been suggested that Intrusions 1 and 2 derived from compositionally different parental melts. Model calculations (COMAGMAT-3.57) show that parental melts of two intrusions were close to high-Mg picrobasaltic magmas. The concentration of MgO in melt is 16.21 (Intrusion 1) and 16.17 (Intrusion 2). Isotopic data of Ortsog-Uul magmatic rocks exhibit different values of εNd (positive and negative) for Intrusion 1 and 2, respectively.

Petrologic, morphologic and functional analyses of ground and abrasive stone tools from Rug Bair, Ovche Pole valley

International Nuclear Information System (INIS)

Boev, Blazho; Dimitrovska, Vasilka

2011-01-01

This paper represents the results of the ground and abrasive stone tools analyses based on the finds collected during the excavation of Rug Bair undertaken in 1970, and today stored in the Museum and Institute for Protection of Shtip. The studies were made possible with the help from the Faculty of Natural and Technical Sciences, Shtip, Republic of Macedonia. Through the stone material, an attempt was made a more comprehensive picture of the raw material, petrologic, technical and typo logical characteristics of the Neolithic stone industry at this site to be gained as well as its relationship with related simultaneously industries. (Author)

Petrology of forearc basalt-related isotropic gabbros from the Bonin Ridge, Izu-Bonin forearc

Science.gov (United States)

Garcia, S. E.; Loocke, M. P.; Snow, J. E.

2017-12-01

The early arc volcanic rocks exposed on the Bonin Ridge (BR), a large forearc massif in the Izu-Bonin arc, have provided us with a natural laboratory for the study of subduction initiation and early arc development. The BR has been the subject of focused sampling by way of dredging, diving, and drilling (IODP EXP352) expeditions which have revealed a composite stratigraphy consisting, from bottom to top, of intercalated peridotites and gabbros, isotropic gabbros, sheeted dykes, and a lava sequence which transitions from forearc basalt (FAB) to more arc-like volcanics up section. Although little has been published regarding the moho-transition zone rocks of the BR in comparison to the volcanic rocks, even less work has been published regarding the isotropic gabbros recovered in close association with FABs. Ishizuka et al. (2011) determined that the isotropic gabbros are compositionally and temporally related to the FABs. We provide the first petrologic characterization, including petrography and electron probe microanalysis, of a suite of FAB-related gabbros recovered by dredge D42 of the 2007 R/V Hakuho Maru KH07-02 dredging cruise. Preliminary petrographic observations of the fourteen thin sections reveal that all of the samples contain variable amounts of relict orthopyroxene and consist of five disseminated oxide gabbros, 5 oxide gabbros, and 2 gabbros. We note that all of the D42 gabbros exhibit strong textural variability akin to the varitextured gabbros described in the dyke-gabbro transition of ophiolites (e.g., MacLeod and Yaouancq, 2000). Geochemical data from this critically understudied horizon have the potential to inform regarding the nature of crustal accretion during subduction initiation and the formation, migration, and evolution of FABs. Further, with many authors comparing the volcanic record and crustal stratigraphy of the BR to ophiolites (e.g., Ishizuka et al., 2014), these data would provide another in situ analogue for comparison with the

Optimization methods for logical inference

CERN Document Server

Chandru, Vijay

2011-01-01

Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though ""solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs."" Presenting powerful, proven optimization techniques for logic in

Inference in `poor` languages

Energy Technology Data Exchange (ETDEWEB)

Petrov, S.

1996-10-01

Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.

Petrology and Wavespeeds in Central Tibet Indicate a Partially Melted Mica-Bearing Crust

Science.gov (United States)

Hacker, B. R.; Ritzwoller, M. H.; Xie, J.

2013-12-01

S-wave speeds and Vp/Vs ratios in the middle to deep crust of Tibet are best explained by a partially melted, mica-bearing middle to lower crust with a subhorizontal to gently dipping foliation. Surface-wave tomography [e.g., Yang et al., 2012; Xie et al., 2013] shows that the central Tibetan Plateau (the Qiangtang block) is characterized by i) slow S-wave speeds of 3.3-3.5 km/s at depths from 20-25 km to 45-50 km, ii) S-wave radial anisotropy of at least 4% (Vsh > Vsv) with stronger anisotropy in the west than the east [Duret et al., 2010], and iii) whole-crust Vp/Vs ratios in the range of 1.73-1.78 [Xu et al., 2013]. The depth of the Curie temperature for magnetite inferred from satellite magnetic measurements [Alsdorf and Nelson, 1999], the depth of the α-β quartz transition inferred from Vp/Vs ratios [Mechie et al., 2004], and the equilibration pressures and temperatures of xenoliths erupted from the mid-deep crust [Hacker et al., 2000] indicate that the thermal gradient in Qiangtang is steep, reaching 1000°C at 30-40 km depth. This thermal gradient crosses the dehydration-melting solidi for crustal rocks at 20-30 km depth, implying the presence or former presence of melt in the mid-deep crust. These temperatures do not require the wholesale breakdown of mica at these depths, because F and Ti can stabilize mica to at least 1300°C [Dooley and Patino Douce, 1996]. Petrology suggests, then, that the Qiangtang middle to deep crust consists of a mica-bearing residue from which melt has been extracted or is being extracted. Wavespeeds calculated for mica-bearing rocks with a subhorizontal to gently dipping foliation and minor silicate melt are the best match to the wavespeeds and anisotropy observed by seismology. Alsdorf, D., and D. Nelson, The Tibetan satellite magnetic low: Evidence for widespread melt in the Tibetan crust?, Geology, 27, 943-946, 1999. Dooley, D.F., and A.F. Patino Douce, Fluid-absent melting of F-rich phlogopite + rutile +quartz, American

Rasa Nirdhāraṇa (assessment of taste of Leonotis nepetifolia (L. R. Br.: A preliminary study in healthy volunteers

Directory of Open Access Journals (Sweden)

Reshmi Pushpan

2014-01-01

Results and Conclusion: On analyzing the data it was found that Leonotis nepetifolia possess predominantly tikta rasa (bitter taste followed by Kasāya rasa (astringent taste. Recent researches and ethnomedicinal claims on Leonotis nepetifolia stand comparable with the pharmacological activities attributed to tikta and kasāya rasa in Ayurvedic classics Rasa nirdhāraṇa can be one of the preliminary steps to initiate the process of screening of an unknown drug along the lines of Ayurvedic pharmacology specially because rasa is the only perceivable parameter. According to Ayurveda, rasa of a dravya has a bearing on its karma (pharmacological action and the identification of rasa could be one of the subjective means for inferring pāρcabhautika constitution of a substance which in turn could help in tentatively inferring guṇa, vîrya and vipāka of the dravya. This paper demonstrates how a simple method can be used without any instruments to do a preliminary assessment of the rasa or taste of a plant.

Shallow-land burial of low-level radioactive wastes: preliminary simulations of long-term health risks

International Nuclear Information System (INIS)

Fields, D.E.; Little, C.A.; Emerson, C.J.; Hiromoto, G.

1982-01-01

PRESTO, a computer code developed for the Environmental Protection Agency for the evaluation of possible health effects associated with shallow-land rad-waste burial areas, has been used to perform simulations for three such sites. Preliminary results for the 1000 y period following site closure suggest that shallow burial, at properly chosen sites, is indeed an appropriate disposal practice for low-level wastes. Periods of maximum risk to subject populations are also inferred

EI: A Program for Ecological Inference

Directory of Open Access Journals (Sweden)

Gary King

2004-09-01

Full Text Available The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King 1997. Ecological inference, as traditionally defined, is the process of using aggregate (i.e., "ecological" data to infer discrete individual-level relationships of interest when individual-level data are not available. Ecological inferences are required in political science research when individual-level surveys are unavailable (e.g., local or comparative electoral politics, unreliable (racial politics, insufficient (political geography, or infeasible (political history. They are also required in numerous areas of ma jor significance in public policy (e.g., for applying the Voting Rights Act and other academic disciplines ranging from epidemiology and marketing to sociology and quantitative history.

EXPLOSION POTENTIAL ASSESSMENT OF HEAT EXCHANGER NETWORK AT THE PRELIMINARY DESIGN STAGE

Directory of Open Access Journals (Sweden)

MOHSIN PASHA

2016-07-01

Full Text Available The failure of Shell and Tube Heat Exchangers (STHE is being extensively observed in the chemical process industries. This failure can cause enormous production loss and have a potential of dangerous consequences such as an explosion, fire and toxic release scenarios. There is an urgent need for assessing the explosion potential of shell and tube heat exchanger at the preliminary design stage. In current work, inherent safety index based approach is used to resolve the highlighted issue. Inherent Safety Index for Shell and Tube Heat Exchanger (ISISTHE is a newly developed index for assessing the inherent safety level of a STHE at the preliminary design stage. This index is composed of preliminary design variables and integrated with the process design simulator (Aspen HYSYS. Process information can easily be transferred from process design simulator to MS Excel spreadsheet owing to this integration. This index could potentially facilitate the design engineer to analyse the worst heat exchanger in the heat exchanger network. Typical heat exchanger network of the steam reforming process is presented as a case study and the worst heat exchanger of this network has been identified. It is inferred from this analysis that shell and tube heat exchangers possess high operating pressure, corrected mean temperature difference (CMTD and flammability and reactive potential needs to be critically analysed at the preliminary design stage.

On the criticality of inferred models

Science.gov (United States)

Mastromatteo, Iacopo; Marsili, Matteo

2011-10-01

Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. This region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time scales naturally yield models which are close to criticality.

On the criticality of inferred models

International Nuclear Information System (INIS)

Mastromatteo, Iacopo; Marsili, Matteo

2011-01-01

Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. This region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time scales naturally yield models which are close to criticality

An Inference Language for Imaging

DEFF Research Database (Denmark)

Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen

2014-01-01

We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framewor...

PETROLOGIC CONSTRAINTS ON AMORPHOUS AND CRYSTALLINE MAGNESIUM SILICATES: DUST FORMATION AND EVOLUTION IN SELECTED HERBIG Ae/Be SYSTEMS

International Nuclear Information System (INIS)

Rietmeijer, Frans J. M.; Nuth, Joseph A.

2013-01-01

The Infrared Space Observatory, Spitzer Space Telescope, and Herschel Space Observatory surveys provided a wealth of data on the Mg-silicate minerals (forsterite, enstatite), silica, and ''amorphous silicates with olivine and pyroxene stoichiometry'' around Herbig Ae/Be stars. These incredible findings do not resonate with the mainstream Earth Sciences because of (1) disconnecting ''astronomical nomenclature'' and the long existing mineralogical and petrologic terminology of minerals and amorphous materials, and (2) the fact that Earth scientists (formerly geologists) are bound by the ''Principle of Actualism'' that was put forward by James Hutton (1726-1797). This principle takes a process-oriented approach to understanding mineral and rock formation and evolution. This paper will (1) review and summarize the results of laboratory-based vapor phase condensation and thermal annealing experiments, (2) present the pathways of magnesiosilica condensates to Mg-silicate mineral (forsterite, enstatite) formation and processing, and (3) present mineralogical and petrologic implications of the properties and compositions of the infrared-observed crystalline and amorphous dust for the state of circumstellar disk evolution. That is, the IR-observation of smectite layer silicates in HD142527 suggests the break-up of asteroid-like parent bodies that had experienced aqueous alteration. We discuss the persistence of amorphous dust around some young stars and an ultrafast amorphous to crystalline dust transition in HD 163296 that leads to forsterite grains with numerous silica inclusions. These dust evolution processes to form forsterite, enstatite ± tridymite could occur due to amorphous magnesiosilica dust precursors with a serpentine- or smectite-dehydroxylate composition.

PETROLOGIC CONSTRAINTS ON AMORPHOUS AND CRYSTALLINE MAGNESIUM SILICATES: DUST FORMATION AND EVOLUTION IN SELECTED HERBIG Ae/Be SYSTEMS

Energy Technology Data Exchange (ETDEWEB)

Rietmeijer, Frans J. M. [Department of Earth and Planetary Sciences, MSC 03 2040, 1-University of New Mexico, Albuquerque, NM 87131-001 (United States); Nuth, Joseph A., E-mail: fransjmr@unm.edu [Astrochemistry Laboratory, Solar System Exploration Division, Code 691, NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States)

2013-07-01

The Infrared Space Observatory, Spitzer Space Telescope, and Herschel Space Observatory surveys provided a wealth of data on the Mg-silicate minerals (forsterite, enstatite), silica, and ''amorphous silicates with olivine and pyroxene stoichiometry'' around Herbig Ae/Be stars. These incredible findings do not resonate with the mainstream Earth Sciences because of (1) disconnecting ''astronomical nomenclature'' and the long existing mineralogical and petrologic terminology of minerals and amorphous materials, and (2) the fact that Earth scientists (formerly geologists) are bound by the ''Principle of Actualism'' that was put forward by James Hutton (1726-1797). This principle takes a process-oriented approach to understanding mineral and rock formation and evolution. This paper will (1) review and summarize the results of laboratory-based vapor phase condensation and thermal annealing experiments, (2) present the pathways of magnesiosilica condensates to Mg-silicate mineral (forsterite, enstatite) formation and processing, and (3) present mineralogical and petrologic implications of the properties and compositions of the infrared-observed crystalline and amorphous dust for the state of circumstellar disk evolution. That is, the IR-observation of smectite layer silicates in HD142527 suggests the break-up of asteroid-like parent bodies that had experienced aqueous alteration. We discuss the persistence of amorphous dust around some young stars and an ultrafast amorphous to crystalline dust transition in HD 163296 that leads to forsterite grains with numerous silica inclusions. These dust evolution processes to form forsterite, enstatite {+-} tridymite could occur due to amorphous magnesiosilica dust precursors with a serpentine- or smectite-dehydroxylate composition.

Inference

DEFF Research Database (Denmark)

Møller, Jesper

2010-01-01

Chapter 9: This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo...... (MCMC) techniques. Due to space limitations the focus is on spatial point processes....

Feature Inference Learning and Eyetracking

Science.gov (United States)

Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

2009-01-01

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

Petrology, magnetostratigraphy and geochronology of the Miocene volcaniclastic Tepoztlán Formation: implications for the initiation of the Transmexican Volcanic Belt (Central Mexico)

OpenAIRE

Lenhardt, Nils; Böhnel, Harald; Wemmer, Klaus; Torres-Alvarado, Ignacio; Hornung, Jens; Hinderer, Matthias

2010-01-01

The volcaniclastic Tepoztlán Formation (TF) represents an important rock record to unravel the early evolution of the Transmexican Volcanic Belt (TMVB). Here, a depositional model together with a chronostratigraphy of this Formation is presented, based on detailed field observations together with new geochronological, paleomagnetic, and petrological data. The TF consists predominantly of deposits from pyroclastic density currents and extensive epiclastic products such as tuffaceous sandstones...

Geology and petrology of the basalts of Crater Flat: applications to volcanic risk assessment for the Nevada Nuclear Waste Storage investigations

International Nuclear Information System (INIS)

Vaniman, D.; Crowe, B.

1981-06-01

Volcanic hazard studies of the south-central Great Basin, Nevada, are being conducted for the Nevada Nuclear Waste Storage Investigations. This report presents the results of field and petrologic studies of the basalts of Crater Flat, a sequence of Pliocene to Quaternary-age volcanic centers located near the southwestern part of the Nevada Test Site. Crater Flat is one of several basaltic fields constituting a north-northeast-trending volcanic belt of Late Cenozoic age extending from southern Death Valley, California, through the Nevada Test Site region to central Nevada. The basalts of Crater Flat are divided into three distinct volcanic cycles. The cycles are characterized by eruption of basalt magma of hawaiite composition that formed cinder cone clusters and associated lava flows. Total volume of erupted magma for respective cycles is given. The basalts of Crater Flat are sparsely to moderately porphyritic; the major phenocryst phase is olivine, with lesser amounts of plagioclase, clinopyroxene, and rare amphibole. The consistent recurrence of evolved hawaiite magmas in all three cycles points to crystal fractionation from more primitive magmas at depth. A possible major transition in mantle source regions through time may be indicated by a transition from normal to Rb-depleted, Sr-enriched hawaiites in the younger basaltic cycles. The recurrence of small volumes of hawaiite magma at Crater Flat supports assumptions required for probability modeling of future volcanic activity and provides a basis for estimating the effects of volcanic disruption of a repository site in the southwestern Nevada Test Site region. Preliminary data suggest that successive basalt cycles at Crater Flat may be of decreasing volume but recurring more frequently

Forward and backward inference in spatial cognition.

Directory of Open Access Journals (Sweden)

Will D Penny

Full Text Available This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.

Petrology of basalts from Loihi Seamount, Hawaii

Science.gov (United States)

Hawkins, James; Melchior, John

1983-12-01

Loihi Seamount is the southeasternmost active volcano of the Emperor-Hawaii linear volcanic chain. It comprises a spectrum of basalt compositional varieties including basanite, alkali basalt, transitional basalt and tholeiite. Samples from four dredge collections made on Scripps Institution of Oceanography Benthic Expedition in October 1982 are tholeiite. The samples include highly vesicular, olivine-rich basalt and dense glass-rich pillow fragments containing olivine and augite phenocrysts. Both quartz-normative and olivine-normative tholeiites are present. Minor and trace element data indicate relatively high abundances of low partition coefficient elements (e.g., Ti, K, P. Rb, Ba, Zr) and suggest that the samples were derived by relatively small to moderate extent of partial melting, of an undepleted mantle source. Olivine composition, MgO, Cr and Ni abundances, and Mg/(Mg+Fe), are typical of moderately fractionated to relatively unfractionated "primary" magmas. The variations in chemistry between samples cannot be adequately explained by low-pressure fractional crystallization but can be satisfied by minor variations in extent of melting if a homogeneous source is postulated. Alternatively, a heterogeneous source with variable abundances of certain trace elements, or mixing of liquids, may have been involved. Data for 3He/ 4He, presented in a separate paper, implies a mantle plume origin for the helium composition of the Loihi samples. There is little variation in the helium isotope ratio for samples having different compositions and textures. The helium data are not distinctive enough to unequivocally separate the magma sources for the tholeiitic rocks from the other rock types such as Loihi alkalic basalts and the whole source region for Loihi may have a nearly uniform helium compositions even though other element abundances may be variable. Complex petrologic processes including variable melting, fractional crystallization and magma mixing may have blurred

Preliminary study of magnet design for an SSC

International Nuclear Information System (INIS)

Taylor, C.E.; Meuser, R.B.

1983-08-01

The overriding design consideration for the SSC magnets is that cost of the facility be minimized; at 8 T, approximately 40 km of bending magnets is required for each ring of a 20 TeV collider. We present some results of a parametric study of two-in-one, iron-core magnets for an SSC. These results are necessarily preliminary in nature, and are intended only to show some of the trade-offs for a wide range of the variables. We show also some results for a reference design that produces 6.5 T in the aperture at 4.4 K for a coil inside diameter of 40 mm. It is not to be inferred that we have established this to be an optimum in any sense

A formal model of interpersonal inference

Directory of Open Access Journals (Sweden)

Michael eMoutoussis

2014-03-01

Full Text Available Introduction: We propose that active Bayesian inference – a general framework for decision-making – can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance. Method: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study. The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference. Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others. The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory. Results: 1. Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred. This inference, akin to 'mentalising' in the psychological literature, is based upon the outcomes of interpersonal exchanges. 2. We show how some well-known social-psychological phenomena (e.g. self-serving biases can be explained in terms of active interpersonal inference. 3. Mentalising naturally entails Bayesian updating of how people value social outcomes. Crucially this includes inference about one’s own qualities and preferences. Conclusion: We inaugurate a Bayes optimal framework for modelling intersubject variability in mentalising during interpersonal exchanges. Here, interpersonal representations are endowed with explicit functional and affective properties. We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalising is distorted.

Distributional Inference

NARCIS (Netherlands)

Kroese, A.H.; van der Meulen, E.A.; Poortema, Klaas; Schaafsma, W.

1995-01-01

The making of statistical inferences in distributional form is conceptionally complicated because the epistemic 'probabilities' assigned are mixtures of fact and fiction. In this respect they are essentially different from 'physical' or 'frequency-theoretic' probabilities. The distributional form is

Continuous Integrated Invariant Inference, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The proposed project will develop a new technique for invariant inference and embed this and other current invariant inference and checking techniques in an...

Rainfall prediction using fuzzy inference system for preliminary micro-hydro power plant planning

Science.gov (United States)

Suprapty, B.; Malani, R.; Minardi, J.

2018-04-01

East Kalimantan is a very rich area with water sources, in the form of river streams that branch to the remote areas. The conditions of natural potency like this become alternative solution for area that has not been reached by the availability of electric energy from State Electricity Company. The river water in selected location (catchment area) which is channelled to the canal, pipeline or penstock can be used to drive the waterwheel or turbine. The amount of power obtained depends on the volume/water discharge and headwater (the effective height between the reservoir and the turbine). The water discharge is strongly influenced by the amount of rainfall. Rainfall is the amount of water falling on the flat surface for a certain period measured, in units of mm3, above the horizontal surface in the absence of evaporation, run-off and infiltration. In this study, the prediction of rainfall is done in the area of East Kalimantan which has 13 watersheds which, in principle, have the potential for the construction of Micro Hydro Power Plant. Rainfall time series data is modelled by using AR (Auto Regressive) Model based on FIS (Fuzzy Inference System). The FIS structure of the training results is then used to predict the next two years rainfall.

Estimating uncertainty of inference for validation

Energy Technology Data Exchange (ETDEWEB)

Booker, Jane M [Los Alamos National Laboratory; Langenbrunner, James R [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Ross, Timothy J [UNM

2010-09-30

We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the

Petrologic testament to changes in shallow magma storage and transport during 30+ years of recharge and eruption at Kīlauea Volcano, Hawai‘i: Chapter 8

Science.gov (United States)

Thornber, Carl R.; Orr, Tim R.; Heliker, Christina; Hoblitt, Richard P.; Carey, Rebecca; Cayol, Valérie; Poland, Michael P.; Weis, Dominique

2015-01-01

Petrologic monitoring of Kīlauea Volcano from January 1983 to October 2013 has yielded an extensive record of glass, phenocryst, melt inclusion, and bulk-lava chemistry from well-quenched lava. When correlated with 30+ years of geophysical and geologic monitoring, petrologic details testify to physical maturation of summit-to-rift magma plumbing associated with sporadic intrusion and prolonged magmatic overpressurization. Changes through time in bulk-lava major- and trace-element compositions, along with glass thermometry, record shifts in the dynamic balance of fractionation, mixing, and assimilation processes inherent to magma storage and transport during near-continuous recharge and eruption. Phenocryst composition, morphology, and texture, along with the sulfur content of melt inclusions, constrain coupled changes in eruption behavior and geochemistry to processes occurring in the shallow magmatic system. For the first 17 years of eruption, magma was steadily tapped from a summit reservoir at 1–4 km depth and circulating between 1180 and 1200°C. Furthermore, magma cooled another 30°C while flowing through the 18 km long rift conduit, before erupting olivine-spinel-phyric lava at temperatures of 1150–1170°C in a pattern linked with edifice deformation, vent formation, eruptive vigor, and presumably the flux of magma into and out of the summit reservoir. During 2000–2001, a fundamental change in steady state eruption petrology to that of relatively low-temperature, low-MgO, olivine(-spinel)-clinopyroxene-plagioclase-phryic lava points to a physical transformation of the shallow volcano plumbing uprift of the vent. Preeruptive comagmatic mixing between hotter and cooler magma is documented by resorption, overgrowth, and compositional zonation in a mixed population of phenocrysts grown at higher and lower temperatures. Large variations of sulfur (50 to >1000 ppm) in melt inclusions within individual phenocrysts and among phenocrysts in most samples

Magnesium isotope evidence for single stage formation of CB chondrules by colliding planetesimals

DEFF Research Database (Denmark)

Olsen, Mia Bjørg Stolberg; Schiller, Martin; Krot, Alexander N.

2013-01-01

Chondrules are igneous spherical objects preserved in chondritic meteorites and believed to have formed during transient heating events in the solar protoplanetary disk. Chondrules present in the metal-rich CB chondrites show unusual chemical and petrologic features not observed in other chondrit...... planetesimals. The inferred μMg* value of -3.87 ± 0.93 ppm for the CB parent body is significantly lower than the bulk solar system value of 4.5 ± 1.1 ppm inferred from CI chondrites, suggesting that CB chondrites accreted material comprising an early formed Al-free component.......Chondrules are igneous spherical objects preserved in chondritic meteorites and believed to have formed during transient heating events in the solar protoplanetary disk. Chondrules present in the metal-rich CB chondrites show unusual chemical and petrologic features not observed in other chondrite......, indicating substantial suppression of isotopic fractionation during evaporative loss of Mg, possibly due to evaporation at high Mg partial pressure. Thus, the Mg-isotope data of skeletal chondrules from HH237 are consistent with their origin as melts produced in the impact-generated plume of colliding...

Quantum-Like Representation of Non-Bayesian Inference

Science.gov (United States)

Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.

2013-01-01

This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.

Bayesian Inference Methods for Sparse Channel Estimation

DEFF Research Database (Denmark)

Pedersen, Niels Lovmand

2013-01-01

This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...

Statistical inference an integrated Bayesianlikelihood approach

CERN Document Server

Aitkin, Murray

2010-01-01

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It pre

Magmatic and petrologic evolution of the mesozvic vulcanic acid rocks from Piraju-Ourinhos region (SP-PR)

International Nuclear Information System (INIS)

Raposo, M.I.B.

1987-01-01

This work presents the result of geological, petrological and geochemical studies, on the volcanic rocks from Piraju-Ourinhos region, SP, with special emphasis on the rocks. A geological mapping was carried out by using images from Landsat satellite. Petrographic and chemical analyses have defined a suite represented by basic lithotype - tholeutic andesibasalt - with high TiO 2 , rich in incompable elements - mainly Sr, Zr, La, Ce, and Ba - and by acid lithotype - rhyolite - rhyodacite. k-Ar ages are determined in feldspar concentrated, and indicate an age of 133+- 4m,y, for the volcanic acid rocks. Determinations of Sr isotopes. In order to explain the genesis of Chapeco type acid magnas quantitative models were tested using both fractional Crystallization [pt

Inference Attacks and Control on Database Structures

Directory of Open Access Journals (Sweden)

Muhamed Turkanovic

2015-02-01

Full Text Available Today’s databases store information with sensitivity levels that range from public to highly sensitive, hence ensuring confidentiality can be highly important, but also requires costly control. This paper focuses on the inference problem on different database structures. It presents possible treats on privacy with relation to the inference, and control methods for mitigating these treats. The paper shows that using only access control, without any inference control is inadequate, since these models are unable to protect against indirect data access. Furthermore, it covers new inference problems which rise from the dimensions of new technologies like XML, semantics, etc.

Type Inference with Inequalities

DEFF Research Database (Denmark)

Schwartzbach, Michael Ignatieff

1991-01-01

of (monotonic) inequalities on the types of variables and expressions. A general result about systems of inequalities over semilattices yields a solvable form. We distinguish between deciding typability (the existence of solutions) and type inference (the computation of a minimal solution). In our case, both......Type inference can be phrased as constraint-solving over types. We consider an implicitly typed language equipped with recursive types, multiple inheritance, 1st order parametric polymorphism, and assignments. Type correctness is expressed as satisfiability of a possibly infinite collection...

A New Approach to Teaching Petrology: Active Learning in a Studio Classroom

Science.gov (United States)

Perkins, D.

2003-12-01

During the past 15 years it has become clear that the traditional lecture and lab approach to college science teaching leaves much to be desired. The traditional approach is instructor oriented and based on passive learning. In contrast, current studies show that most students learn best when actively engaged in the learning process. Inquiry based learning and open ended projects have been shown to especially enhance learning by promoting higher order thinking. Recognizing the need for change, however, does not mean the changes are simple. The task of overhauling a course, replacing traditional approaches with more student oriented activities, requires a great deal of time and effort. It also involves much uncertainty and risk. At UND we have been experimenting with alternative pedagogies for a number of years. Change has been incremental, but this year we made wholesale changes in our petrology class. We converted it from the standard three lecture and one lab format to two 3-hour studio sessions per week. The distinction between lab and lecture is gone. In fact, there really are no lectures. The instructor talks for no more than 15 or 20 minutes at a time. Students spend most of their time doing, not listening. We emphasize collaborative active learning projects, some quite short and others lengthy and involved, and use a wide variety of activities. To assess the class, we have an outside consultant and we carry out weekly assessments to measure (1) how students are reacting to the various pedagogical approaches, and (2) how much student learning is actually occurring. This allows us to make adjustments and fine tune as necessary. We could not have made such changes a few years ago, simply because of the amount of work involved to create and test the necessary classroom materials. Today, however, there are many resources available to the reform minded teacher, and the resource base continues to grow. We borrowed heavily from other instructors at other

Padrt'stock (Teplá–Barrandian unit, Bohemian Massif): Petrology, geochemistry, U-Pb zircon dating of granodiorite, and Re-Os age and origin of related molybdenite mineralization

Czech Academy of Sciences Publication Activity Database

Žák, Karel; Svojtka, Martin; Breiter, Karel; Ackerman, Lukáš; Zachariáš, J.; Pašava, J.; Veselovský, F.; Litochleb, J.; Ďurišová, Jana; Haluzová, Eva

2014-01-01

Roč. 59, č. 4 (2014), s. 351-366 ISSN 1802-6222 R&D Projects: GA ČR GA13-15390S Institutional support: RVO:67985831 Keywords : geochemistry * petrology * Re–Os molybdenite dating * Teplá–barrandian unit * U–Pb zircon dating * Variscan granitoids Subject RIV: DD - Geochemistry Impact factor: 1.405, year: 2014

Inference in models with adaptive learning

NARCIS (Netherlands)

Chevillon, G.; Massmann, M.; Mavroeidis, S.

2010-01-01

Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be

Fiducial inference - A Neyman-Pearson interpretation

NARCIS (Netherlands)

Salome, D; VonderLinden, W; Dose,; Fischer, R; Preuss, R

1999-01-01

Fisher's fiducial argument is a tool for deriving inferences in the form of a probability distribution on the parameter space, not based on Bayes's Theorem. Lindley established that in exceptional situations fiducial inferences coincide with posterior distributions; in the other situations fiducial

Uncertainty in prediction and in inference

NARCIS (Netherlands)

Hilgevoord, J.; Uffink, J.

1991-01-01

The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close re-lationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in

Polynomial Chaos Surrogates for Bayesian Inference

KAUST Repository

Le Maitre, Olivier

2016-01-06

The Bayesian inference is a popular probabilistic method to solve inverse problems, such as the identification of field parameter in a PDE model. The inference rely on the Bayes rule to update the prior density of the sought field, from observations, and derive its posterior distribution. In most cases the posterior distribution has no explicit form and has to be sampled, for instance using a Markov-Chain Monte Carlo method. In practice the prior field parameter is decomposed and truncated (e.g. by means of Karhunen- Lo´eve decomposition) to recast the inference problem into the inference of a finite number of coordinates. Although proved effective in many situations, the Bayesian inference as sketched above faces several difficulties requiring improvements. First, sampling the posterior can be a extremely costly task as it requires multiple resolutions of the PDE model for different values of the field parameter. Second, when the observations are not very much informative, the inferred parameter field can highly depends on its prior which can be somehow arbitrary. These issues have motivated the introduction of reduced modeling or surrogates for the (approximate) determination of the parametrized PDE solution and hyperparameters in the description of the prior field. Our contribution focuses on recent developments in these two directions: the acceleration of the posterior sampling by means of Polynomial Chaos expansions and the efficient treatment of parametrized covariance functions for the prior field. We also discuss the possibility of making such approach adaptive to further improve its efficiency.

Petrologic perspectives on tectonic evolution of a nascent basin (Okinawa Trough) behind Ryukyu Arc:A review

Institute of Scientific and Technical Information of China (English)

YAN Quanshu; SHI Xuefa

2014-01-01

Okinawa Trough is a back-arc, initial marginal sea basin, located behind the Ryukyu Arc-Trench System. The formation and evolution of the Okinawa Trough is intimately related to the subduction process of the Philippine Sea Plate beneath the Eurasian Plate since the late Miocene. The tectonic evolution of the trough is similar to other active back-arcs, such as the Mariana Trough and southern Lau Basin, all of which are experiencing the initial rifting and subsequent spreading process. This study reviews all petrologic and geochemical data of mafic volcanic lavas from the Okinawa Trough, Ryukyu Arc, and Philippine Sea Plate, combined with geophysical data to indicate the relationship between the subduction sources (input) and arc or back-arc magmas (output) in the Philippine Sea Plate-Ryukyu Arc-Okinawa Trough system (PROS). The results obtained showed that several components were variably involved in the petrogenesis of the Oki-nawa Trough lavas:sub-continental lithospheric mantle underlying the Eurasian Plate, Indian mid-oceanic ridge basalt (MORB)-type mantle, and Pacific MORB-type mantle. The addition of shallow aqueous fluids and deep hydrous melts from subducted components with the characteristics of Indian MORB-type mantle into the mantle source of lavas variably modifies the primitive mantle wedge beneath the Ryukyu and sub-continental lithospheric mantle (SCLM) beneath the Okinawa Trough. In the northeastern end of the trough and arc, instead of Indian MORB-type mantle, Pacific MORB-type mantle dominates the magma source. Along the strike of the Ryukyu Arc and Okinawa Trough, the systematic variations in trace element ratios and isotopic compositions reflect the first-order effect of variable subduction input on the magma source. In general, petrologic data, combined with geophysical data, imply that the Okinawa Trough is experiencing the“seafloor spreading”process in the southwest segment,“rift propagation”process in the middle seg-ment, and�

Interactive Instruction in Bayesian Inference

DEFF Research Database (Denmark)

Khan, Azam; Breslav, Simon; Hornbæk, Kasper

2018-01-01

An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These pri......An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction....... These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....

Inferring Phylogenetic Networks Using PhyloNet.

Science.gov (United States)

Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

2018-07-01

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

Author Details

African Journals Online (AJOL)

Petrology of the Cenomanian Upper Member of the Mamfe Embayment, southwestern Cameroon Details · Vol 38, No 1 (2002) - Articles Sequence stratigraphy of Iso field, western onshore Niger Delta, Nigeria Details · Vol 39, No 2 (2003) - Articles Preliminary studies on the lithostratigraphy and depositional environment of ...

Active inference and learning.

Science.gov (United States)

Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni

2016-09-01

This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Active Inference, homeostatic regulation and adaptive behavioural control.

Science.gov (United States)

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl

2015-11-01

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Generative Inferences Based on Learned Relations

Science.gov (United States)

Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.

2017-01-01

A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…

An experimental and petrologic investigation of the source regions of lunar magmatism in the context of the primordial differentiation of the moon

Science.gov (United States)

Elardo, Stephen M.

The primordial differentiation of the Moon via a global magma ocean has become the paradigm under which all lunar data are interpreted. The success of this model in explaining multiple geochemical, petrologic, and isotopic characteristics lunar geology has led to magma oceans becoming the preferred model for the differentiation of Earth, Mars, Mercury, Vesta, and other large terrestrial bodies. The goal of this work is to combine petrologic analyses of lunar samples with high pressure, high temperature petrologic experiments to place new and detailed constraints the petrogenetic processes that operated during different stages of lunar magmatism, the processes that have acted upon these magmas to obscure their relationship to their mantle source regions, and how those source regions fit into the context of the lunar magma ocean model. This work focuses on two important phases of lunar magmatism: the ancient crust-building plutonic lithologies of the Mg-suite dating to ~4.3 Ga, and the most recent known mare basaltic magmas dating to ~3 Ga. These samples provide insight into the petrogenesis of magmas and interior thermal state when the Moon was a hot, juvenile planet, and also during the last gasps of magmatism from a cooling planet. Chapter 1, focusing on Mg-suite troctolite 76535, presents data on chromite symplectites, olivine-hosted melt inclusions, intercumulus mineral assemblages, and cumulus mineral chemistry to argue that the 76535 was altered by metasomatism by a migrating basaltic melt. This process could effectively raise radioisotope systems above their mineral-specific blocking temperatures and help explain some of the Mg-suite-FAN age overlap. Chapter 2 focuses on lunar meteorites NWA 4734, 032, and LAP 02205, which are 3 of the 5 youngest igneous samples from the Moon. Using geochemical and isotopic data combined with partial melting models, it is shown that these basalts do not have a link to the KREEP reservoir, and a model is presented for low

Incorporating Problem-Based Learning Into A Petrology Course Through A Research Project In The Local Northern Sierra Nevada

Science.gov (United States)

Aird, H. M.

2016-12-01

A research project into the local petrology was integrated into the Spring 2016 Petrology and Optical Mineralogy course at California State University, Chico. This is a required majors course, typically taken during spring of the junior year, with an enrollment of 10-20 students. Since the labs for this course have a strong focus on petrography, a research project was introduced to give students experience in using a multi-faceted approach to investigate a problem. In many cases, this is their first taste of research. During the first week of the Spring 2016 class, students were introduced to the research question: In the broader context of Californian tectonic history, are the Bucks Lake and Grizzly plutons of the northern Sierra Nevada petrogenetically related? With faculty guidance over the course of the semester, students carried out fieldwork and sampling, lithologic description, selection of the best samples for further analysis, thin section production, petrographic description, and analysis and interpretation of published geochemical data. Research activities were strategically scheduled within the course framework such that students were academically prepared to carry out each task. Each student was responsible for generating all the data for one sample, and data were then collated as a class, so students wrote their individual final reports using all the data collected by the class. Careful scaffolding of writing assignments throughout the semester guided students through the preparation of an academic-style scientific report, while allowing for repeated feedback on their writing style and content. In mid-May, the class presented a group poster at the College of Natural Sciences annual poster symposium, and were awarded `Best Student Class Project' by the judges. Anecdotal student feedback indicated they highly valued the research experience and some were inspired to pursue individual undergraduate research projects under faculty supervision.

Organic petrology in the service of archaeology

Energy Technology Data Exchange (ETDEWEB)

Teichmueller, M.

1992-02-01

The techniques of organic petrology have been used to study the nature and provenance of 81 ornaments ranging in age from the Celtic (dated as late Hallstatt-early La Tene times, 500-300 B.C.) to Roman (1st-4th Century A.D.) periods, which have been recovered from graves and settlements in Germany and Switzerland. The ornaments were mainly black or dark brown armlets but also included beads, buttons and medallions. The most commonly used source material was jet (22 objects) which is derived from bituminized drift woods found in Liassic oil shale, probably mainly of English provenance. Eleven objects were made from Carboniferous cannel coal and four from boghead coal, both of unknown geographic provenance. Only one object proved to be made from {ital Posidonia} shale, a liassic oil shale from southern Germany. The identification of two distinctive sapropelites, used mainly for the production of armlets, is of particular interest. These sapropelites are the 'Schwarte' from the top of the Kounova Seam (Stephanian) of northern Bohemia (15 objects) and the Kimmeridge 'coal' of Dorset, England (19 objects). The recognition of these organic materials was made possible by the study of fresh rock samples. All the armlets made from 'Schwarte' were excavated in the Celtic oppidum at Manching in southern Germany; the armlets made from Kimmeridge 'coal' were found in Celtic and Roman graves. These discoveries suggest the existence of early trade routes crossing the English Channel and passing south to Switzerland, probably along the River Rhine. A very few armlets were made from dark tuff (probably of Bohemian origin), black glass or dark brown bone. These materials were probably all used as substitutes for jet and/or sapropelites. It is interesting to note that all these dark armlets were thought to possess magic properties, which may explain their frequency. 26 refs., 4 figs., 4 tabs.

Parametric statistical inference basic theory and modern approaches

CERN Document Server

Zacks, Shelemyahu; Tsokos, C P

1981-01-01

Parametric Statistical Inference: Basic Theory and Modern Approaches presents the developments and modern trends in statistical inference to students who do not have advanced mathematical and statistical preparation. The topics discussed in the book are basic and common to many fields of statistical inference and thus serve as a jumping board for in-depth study. The book is organized into eight chapters. Chapter 1 provides an overview of how the theory of statistical inference is presented in subsequent chapters. Chapter 2 briefly discusses statistical distributions and their properties. Chapt

Magma transport and storage at Mt. Etna (Italy): A review of geodetic and petrological data for the 2002-03, 2004 and 2006 eruptions

Science.gov (United States)

Palano, Mimmo; Viccaro, Marco; Zuccarello, Francesco; Gresta, Stefano

2017-11-01

A detailed reconstruction of magma movements within the plumbing system of Mt. Etna volcano has been made by reviewing the eruptions occurring during the October 2002-December 2006 period. The availability of continuous GPS data allowed detecting at least ten different ground deformation stages, highlighting deflationary and inflationary episodes as well as the occurrence of a shallow dike intrusion. These data have been coupled with the available petrological datasets including major/trace elements and Sr-Nd-Pb isotope compositions for the volcanic rocks erupted in the 2002-2006 period. We identified two main magmatic reservoirs located at different depths along the plumbing system of the volcano. The former is located at a depth of 7 km bsl and fed the 2001 and 2002-03 eruptions, while the latter, located from 3.5 to 5.5 km bsl, fed the 2004-05 and 2006 eruptions. Petrological characteristics of emitted products have been correlated with the inflation vs. deflation cycles related to the identified sources, providing evidence for changes through time of the evolutionary degree of the erupted magmas along with variations in their geochemical feature. Finally, we suggest that a modification of the deep plumbing system of the volcano might have occurred during the 2002-03 eruption, as a consequence of the major seaward motion of the eastern flank of the volcano.

Variational inference & deep learning: A new synthesis

OpenAIRE

Kingma, D.P.

2017-01-01

In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.

Variational inference & deep learning : A new synthesis

NARCIS (Netherlands)

Kingma, D.P.

2017-01-01

In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.

The Carboniferous to Jurassic evolution of the pre-Alpine basement of Crete: Constraints from U-Pb and U-(Th)-Pb dating of orthogneiss, fission-track dating of zircon, structural and petrological data

Czech Academy of Sciences Publication Activity Database

Romano, S. S.; Brix, M. R.; Dörr, K.; Fiala, Jiří; Krenn, E.; Zulauf, G.

2006-01-01

Roč. 260, - (2006), s. 69-90 ISSN 0375-6440 Institutional research plan: CEZ:AV0Z30130516 Keywords : tectonic-evolution * Carboniferous * Jurassic * uranium-lead-dating * orthogneiss * fission-track-dating * zircon * structural-geology * petrology * metamorphism * high-temperature Subject RIV: DB - Geology ; Mineralogy

Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

Science.gov (United States)

Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

2017-07-01

Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

Ensemble stacking mitigates biases in inference of synaptic connectivity

Directory of Open Access Journals (Sweden)

Brendan Chambers

2018-03-01

Full Text Available A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures

Constraint Satisfaction Inference : Non-probabilistic Global Inference for Sequence Labelling

NARCIS (Netherlands)

Canisius, S.V.M.; van den Bosch, A.; Daelemans, W.; Basili, R.; Moschitti, A.

2006-01-01

We present a new method for performing sequence labelling based on the idea of using a machine-learning classifier to generate several possible output sequences, and then applying an inference procedure to select the best sequence among those. Most sequence labelling methods following a similar

Petrological and zircon evidence for the Early Cretaceous granulite-facies metamorphism in the Dabie orogen, China

Science.gov (United States)

Gao, Xiao-Ying; Zhang, Qiang-Qiang; Zheng, Yong-Fei; Chen, Yi-Xiang

2017-07-01

An integrated study of petrology, mineralogy, geochemistry, and geochronology was carried out for contemporaneous mafic granulite and diorite from the Dabie orogen. The results provide evidence for granulite-facies reworking of the ultrahigh-pressure (UHP) metamorphic rock in the collisional orogen. Most zircons from the granulite are new growth, and their U-Pb ages are clearly categorized into two groups at 122-127 Ma and 188 ± 2 Ma. Although these two groups of zircons show similarly steep HREE patterns and variably negative Eu anomalies, the younger group has much higher U, Th and REE contents and Th/U ratios, much lower εHf(t) values than the older group. This suggests their growth is associated with different types of dehydration reactions. The older zircon domains contain mineral inclusions of garnet + clinopyroxene ± quartz, indicating their growth through metamorphic reactions at high pressures. In contrast, the young zircon domains only contain a few quartz inclusions and the garnet-clinopyroxene-plagioclase-quartz barometry yields pressures of 4.9 to 12.5 kb. In addition, the clinopyroxene-garnet Fe-Mg exchange thermometry gives temperatures of 738-951 °C. Therefore, the young zircon domains would have grown through peritectic reaction at low to medium pressures. The younger granulite-facies metamorphic age is in agreement not only with the adjacent diorite at 125 ± 1 Ma in this study but also the voluminous emplacement of coeval mafic and felsic magmas in the Dabie orogen. Mineral separates from both mafic granulite and its adjacent diorite show uniformly lower δ18O values than normal mantle, similar to those for UHP eclogite-facies metaigneous rocks in the Dabie orogen. In combination with major-trace elements and zircon Lu-Hf isotope compositions, it is inferred that the protolith of mafic granulites shares with the source rock of diorites, both being a kind of mafic metasomatites at the slab-mantle interface in the continental subduction channel

Reasoning about Informal Statistical Inference: One Statistician's View

Science.gov (United States)

Rossman, Allan J.

2008-01-01

This paper identifies key concepts and issues associated with the reasoning of informal statistical inference. I focus on key ideas of inference that I think all students should learn, including at secondary level as well as tertiary. I argue that a fundamental component of inference is to go beyond the data at hand, and I propose that statistical…

Meta-learning framework applied in bioinformatics inference system design.

Science.gov (United States)

Arredondo, Tomás; Ormazábal, Wladimir

2015-01-01

This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

Statistical inference and Aristotle's Rhetoric.

Science.gov (United States)

Macdonald, Ranald R

2004-11-01

Formal logic operates in a closed system where all the information relevant to any conclusion is present, whereas this is not the case when one reasons about events and states of the world. Pollard and Richardson drew attention to the fact that the reasoning behind statistical tests does not lead to logically justifiable conclusions. In this paper statistical inferences are defended not by logic but by the standards of everyday reasoning. Aristotle invented formal logic, but argued that people mostly get at the truth with the aid of enthymemes--incomplete syllogisms which include arguing from examples, analogies and signs. It is proposed that statistical tests work in the same way--in that they are based on examples, invoke the analogy of a model and use the size of the effect under test as a sign that the chance hypothesis is unlikely. Of existing theories of statistical inference only a weak version of Fisher's takes this into account. Aristotle anticipated Fisher by producing an argument of the form that there were too many cases in which an outcome went in a particular direction for that direction to be plausibly attributed to chance. We can therefore conclude that Aristotle would have approved of statistical inference and there is a good reason for calling this form of statistical inference classical.

Children's and adults' judgments of the certainty of deductive inferences, inductive inferences, and guesses.

Science.gov (United States)

Pillow, Bradford H; Pearson, Raeanne M; Hecht, Mary; Bremer, Amanda

2010-01-01

Children and adults rated their own certainty following inductive inferences, deductive inferences, and guesses. Beginning in kindergarten, participants rated deductions as more certain than weak inductions or guesses. Deductions were rated as more certain than strong inductions beginning in Grade 3, and fourth-grade children and adults differentiated strong inductions, weak inductions, and informed guesses from pure guesses. By Grade 3, participants also gave different types of explanations for their deductions and inductions. These results are discussed in relation to children's concepts of cognitive processes, logical reasoning, and epistemological development.

Origins of cratonic mantle discontinuities: A view from petrology, geochemistry and thermodynamic models

Science.gov (United States)

Aulbach, Sonja; Massuyeau, Malcolm; Gaillard, Fabrice

2017-01-01

Geophysically detectible mid-lithospheric discontinuities (MLD) and lithosphere-asthenosphere boundaries (LAB) beneath cratons have received much attention over recent years, but a consensus on their origin has not yet emerged. Cratonic lithosphere composition and origin is peculiar due to its ultra-depletion during plume or accretionary tectonics, cool present-day geothermal gradients, compositional and rheological stratification and multiple metasomatic overprints. Bearing this in mind, we integrate current knowledge on the physical properties, chemical composition, mineralogy and fabric of cratonic mantle with experimental and thermodynamic constraints on the formation and migration of melts, both below and within cratonic lithosphere, in order to find petrologically viable explanations for cratonic mantle discontinuities. LABs characterised by strong seismic velocity gradients and increased conductivity require the presence of melts, which can form beneath intact cratonic roots reaching to 200-250 km depth only in exceptionally warm and/or volatile-rich mantle, thus explaining the paucity of seismical LAB observations beneath cratons. When present, pervasive interaction of these - typically carbonated - melts with the deep lithosphere leads to densification and thermochemical erosion, which generates topography at the LAB and results in intermittent seismic LAB signals or conflicting seismic, petrologic and thermal LAB depths. In rare cases (e.g. Tanzanian craton), the tops of live melt percolation fronts may appear as MLDs and, after complete lithosphere rejuvenation, may be sites of future, shallower LABs (e.g. North China craton). Since intact cratons are presently tectonomagmatically quiescent, and since MLDs produce both positive and negative velocity gradients, in some cases with anisotropy, most MLDs may be best explained by accumulations (metasomes) of seismically slow minerals (pyroxenes, phlogopite, amphibole, carbonates) deposited during past

Deep Learning for Population Genetic Inference.

Science.gov (United States)

Sheehan, Sara; Song, Yun S

2016-03-01

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

The Marbat metamorphic core-complex (Southern Arabian Peninsula) : reassessment of the evolution of a Neoproterozoic island-arc from petrological, geochemical and U-Pb zircon data

OpenAIRE

Barbey, P.; Denele, Y.; Paquette, J. L.; Berger, J.; Ganne, Jérôme; Roques, D.

2018-01-01

The Marbat basement (Sultanate of Oman) belongs to the Neoproterozoic accretion domain of the Arabian-Nubian shield. We present new geochronological, petrological and geochemical data as an extension of our previous study (Denele et al., 2017) re-interpreting this basement as a metamorphic core complex (MCC). We showed that this MCC consists of a metamorphic unit (Juffa complex) separated by an extensional detachment from a plutonic unit (Sadh complex and Tonalite plutons). Geochemical data s...

Using Alien Coins to Test Whether Simple Inference Is Bayesian

Science.gov (United States)

Cassey, Peter; Hawkins, Guy E.; Donkin, Chris; Brown, Scott D.

2016-01-01

Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we…

On Maximum Entropy and Inference

Directory of Open Access Journals (Sweden)

Luigi Gresele

2017-11-01

Full Text Available Maximum entropy is a powerful concept that entails a sharp separation between relevant and irrelevant variables. It is typically invoked in inference, once an assumption is made on what the relevant variables are, in order to estimate a model from data, that affords predictions on all other (dependent variables. Conversely, maximum entropy can be invoked to retrieve the relevant variables (sufficient statistics directly from the data, once a model is identified by Bayesian model selection. We explore this approach in the case of spin models with interactions of arbitrary order, and we discuss how relevant interactions can be inferred. In this perspective, the dimensionality of the inference problem is not set by the number of parameters in the model, but by the frequency distribution of the data. We illustrate the method showing its ability to recover the correct model in a few prototype cases and discuss its application on a real dataset.

The depositional environment and petrology of the White Rim Sandstone Member of the Permian Cutler Formation, Canyonlands National Park, Utah

Science.gov (United States)

Steele-Mallory, B. A.

1982-01-01

The White Rim Sandstone Member of the Cutler Formation of Permian age in Canyonlands National Park, Utah, was deposited in coastal eolian and associated interdune environments. This conclusion is based on stratigraphic relationships primary sedimentary structures, and petrologic features. The White Rim consists of two major genetic units. The first represents a coastal dune field and the second represents related interdune ponds. Distinctive sedimentary structures of the coastal dune unit include large- to medium-scale, unidirectional, tabular-planar cross-bedding; high-index ripples oriented parallel to dip direction of the foresets; coarse-grained lag layers; avalanche or slump marks; and raindrop impressions. Cross-bedding measurements suggest the dunes were deposited as transverse ridges by a dominantly northwest to southeast wind. Distinctive sedimentary structures of the interdune pond unit include wavy, horizontally laminated bedding, adhesion ripples, and desiccation polygons. These features may have been produced by alternate wetting and drying of sediment during water-table fluctuations. Evidence of bioturbation is also present in this unit. Petrologic characteristics of the White Rim helped to define the depositional environment as coastal. A crinoid fragment was identified at one location; both units are enriched in heavy minerals, and small amounts of well rounded, reworked glauconite were found in the White Rim throughout the study area. Earlier work indicates that the White Rim sandstone is late Wolfcampian to early Leonardian in age. During this time, the Canyonlands area was located in a depositional area alternately dominated by marine and nonmarine environments. Results of this study suggest the White Rim represents a coastal dune field that was deposited by predominantly on-shore winds during a period of marine transgression.

Compiling Relational Bayesian Networks for Exact Inference

DEFF Research Database (Denmark)

Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan

2004-01-01

We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating...

Causal inference in economics and marketing.

Science.gov (United States)

Varian, Hal R

2016-07-05

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

Uncertainty in prediction and in inference

International Nuclear Information System (INIS)

Hilgevoord, J.; Uffink, J.

1991-01-01

The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close relationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in inference can be obtained by means of the so-called statistical distance between probability distributions. When applied to quantum mechanics, this distance leads to a measure of the distinguishability of quantum states, which essentially is the absolute value of the matrix element between the states. The importance of this result to the quantum mechanical uncertainty principle is noted. The second part of the paper provides a derivation of the statistical distance on the basis of the so-called method of support

Determination of Indonesian palm-oil-based bioenergy sustainability indicators using fuzzy inference system

Science.gov (United States)

Arkeman, Y.; Rizkyanti, R. A.; Hambali, E.

2017-05-01

Development of Indonesian palm-oil-based bioenergy faces an international challenge regarding to sustainability issue, indicated by the establishment of standards on sustainable bioenergy. Currently, Indonesia has sustainability standards limited to palm-oil cultivation, while other standards are lacking appropriateness for Indonesian palm-oil-based bioenergy sustainability regarding to real condition in Indonesia. Thus, Indonesia requires sustainability indicators for Indonesian palm-oil-based bioenergy to gain recognition and easiness in marketing it. Determination of sustainability indicators was accomplished through three stages, which were preliminary analysis, indicator assessment (using fuzzy inference system), and system validation. Global Bioenergy partnership (GBEP) was used as the standard for the assessment because of its general for use, internationally accepted, and it contained balanced proportion between environment, economic, and social aspects. Result showed that the number of sustainability indicators using FIS method are 21 indicators. The system developed has an accuracy of 85%.

Nonparametric predictive inference in statistical process control

NARCIS (Netherlands)

Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.

2000-01-01

New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on

Compiling Relational Bayesian Networks for Exact Inference

DEFF Research Database (Denmark)

Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark

2006-01-01

We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference...

Making inference from wildlife collision data: inferring predator absence from prey strikes

Directory of Open Access Journals (Sweden)

Peter Caley

2017-02-01

Full Text Available Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.

Making inference from wildlife collision data: inferring predator absence from prey strikes.

Science.gov (United States)

Caley, Peter; Hosack, Geoffrey R; Barry, Simon C

2017-01-01

Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.

Causal inference in biology networks with integrated belief propagation.

Science.gov (United States)

Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

Efficient Bayesian inference for ARFIMA processes

Science.gov (United States)

Graves, T.; Gramacy, R. B.; Franzke, C. L. E.; Watkins, N. W.

2015-03-01

Many geophysical quantities, like atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long-range dependence (LRD). LRD means that these quantities experience non-trivial temporal memory, which potentially enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LRD. In this paper we present a modern and systematic approach to the inference of LRD. Rather than Mandelbrot's fractional Gaussian noise, we use the more flexible Autoregressive Fractional Integrated Moving Average (ARFIMA) model which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LRD, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g. short memory effects) can be integrated over in order to focus on long memory parameters, and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data, with favorable comparison to the standard estimators.

The petrologic evolution and pre-eruptive conditions of the rhyolitic Kos Plateau Tuff (Aegean arc)

Science.gov (United States)

Bachmann, Olivier

2010-09-01

The Kos Plateau Tuff is a large (>60 km3) and young (160 k.y.) calc-alkaline, high-SiO2 rhyolitic ignimbrite from the active Kos-Nisyros volcanic center in the Aegean arc (Greece). Combined textural, petrological and geochemical information suggest that (1) the system evolved dominantly by crystal fractionation from (mostly unerupted) more mafic parents, (2) the magma chamber grew over ≥ 250 000 years at shallow depth (˜1.5-2.5 kb) and was stored as a H2O-rich crystalline mush close to its solidus (˜670-750°C), (3) the eruption occurred after a reheating event triggered by the intrusion of hydrous mafic magma at the base of the rhyolitic mush. Rare banded pumices indicate that the mafic magma only mingled with a trivial portion of resident crystal-rich rhyolite; most of the mush was remobilized following partial melting of quartz and feldspars induced by advection of heat and volatiles from the underplated, hotter mafic influx.

Deep Learning for Population Genetic Inference.

Directory of Open Access Journals (Sweden)

Sara Sheehan

2016-03-01

Full Text Available Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data to the output (e.g., population genetic parameters of interest. We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history. Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

Deep Learning for Population Genetic Inference

Science.gov (United States)

Sheehan, Sara; Song, Yun S.

2016-01-01

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908

A Bayesian Network Schema for Lessening Database Inference

National Research Council Canada - National Science Library

Chang, LiWu; Moskowitz, Ira S

2001-01-01

.... The authors introduce a formal schema for database inference analysis, based upon a Bayesian network structure, which identifies critical parameters involved in the inference problem and represents...

Type Inference for Session Types in the Pi-Calculus

DEFF Research Database (Denmark)

Graversen, Eva Fajstrup; Harbo, Jacob Buchreitz; Huttel, Hans

2014-01-01

In this paper we present a direct algorithm for session type inference for the π-calculus. Type inference for session types has previously been achieved by either imposing limitations and restriction on the π-calculus, or by reducing the type inference problem to that for linear types. Our approach...

Explanatory Preferences Shape Learning and Inference.

Science.gov (United States)

Lombrozo, Tania

2016-10-01

Explanations play an important role in learning and inference. People often learn by seeking explanations, and they assess the viability of hypotheses by considering how well they explain the data. An emerging body of work reveals that both children and adults have strong and systematic intuitions about what constitutes a good explanation, and that these explanatory preferences have a systematic impact on explanation-based processes. In particular, people favor explanations that are simple and broad, with the consequence that engaging in explanation can shape learning and inference by leading people to seek patterns and favor hypotheses that support broad and simple explanations. Given the prevalence of explanation in everyday cognition, understanding explanation is therefore crucial to understanding learning and inference. Copyright © 2016 Elsevier Ltd. All rights reserved.

Grammatical inference algorithms, routines and applications

CERN Document Server

Wieczorek, Wojciech

2017-01-01

This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed. Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others. Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>.

BagReg: Protein inference through machine learning.

Science.gov (United States)

Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou

2015-08-01

Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.

Petrology and geochemistry of greywackes of Middle Aravalli supergroup, NW India: evidence for active margin processes

International Nuclear Information System (INIS)

Absar, Nurul; Sreenivas, B.

2013-01-01

Aravalli Mountain Belt (AMB) of Northwestern, India represents one of the major Proterozoic accretionary orogens of the world, preserving two Wilson cycles; viz. Paleoproterozoic Aravalli Supergroup and Mesoproterozoic Delhi Supergroup. Although two gross Wilson cycles involving opening and closing of Paleoproterozoic Aravalli ocean and Mesoproterozoic Delhi ocean are recognized, the finer details of the evolution of the orogen are still poorly understood. We have carried out geochemical and petrological study of the well-preserved greywacke horizon of the 'Middle Aravalli Supergroup' in order to place constraints on early evolution of the Aravalli basin. These greywackes are enriched in Fe, Mg and K; and depleted in Na in comparison to normal greywackes and can be classified as ferroan potassic sandstone. Petrographic examination indicate that the greywacke samples contain about 30 to 50% matrix that is mainly composed of biotite/chlorite and interspersed with fine Fe-Ti rich opaque mineral phases

Ensemble stacking mitigates biases in inference of synaptic connectivity.

Science.gov (United States)

Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

2018-01-01

A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

Stochastic processes inference theory

CERN Document Server

Rao, Malempati M

2014-01-01

This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.

Russell and Humean Inferences

Directory of Open Access Journals (Sweden)

João Paulo Monteiro

2001-12-01

Full Text Available Russell's The Problems of Philosophy tries to establish a new theory of induction, at the same time that Hume is there accused of an irrational/ scepticism about induction". But a careful analysis of the theory of knowledge explicitly acknowledged by Hume reveals that, contrary to the standard interpretation in the XXth century, possibly influenced by Russell, Hume deals exclusively with causal inference (which he never classifies as "causal induction", although now we are entitled to do so, never with inductive inference in general, mainly generalizations about sensible qualities of objects ( whether, e.g., "all crows are black" or not is not among Hume's concerns. Russell's theories are thus only false alternatives to Hume's, in (1912 or in his (1948.

Efficient algorithms for conditional independence inference

Czech Academy of Sciences Publication Activity Database

Bouckaert, R.; Hemmecke, R.; Lindner, S.; Studený, Milan

2010-01-01

Roč. 11, č. 1 (2010), s. 3453-3479 ISSN 1532-4435 R&D Projects: GA ČR GA201/08/0539; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : conditional independence inference * linear programming approach Subject RIV: BA - General Mathematics Impact factor: 2.949, year: 2010 http://library.utia.cas.cz/separaty/2010/MTR/studeny-efficient algorithms for conditional independence inference.pdf

State-Space Inference and Learning with Gaussian Processes

OpenAIRE

Turner, R; Deisenroth, MP; Rasmussen, CE

2010-01-01

18.10.13 KB. Ok to add author version to spiral, authors hold copyright. State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. C...

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.

Science.gov (United States)

Chen, Daizhuo; Fraiberger, Samuel P; Moakler, Robert; Provost, Foster

2017-09-01

Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from "Likes" on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the "cloaking device"-a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users.

Fused Regression for Multi-source Gene Regulatory Network Inference.

Directory of Open Access Journals (Sweden)

Kari Y Lam

2016-12-01

Full Text Available Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR

International Nuclear Information System (INIS)

Schneider, Michael D.; Dawson, William A.; Hogg, David W.; Marshall, Philip J.; Bard, Deborah J.; Meyers, Joshua; Lang, Dustin

2015-01-01

Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics

Inverse Ising inference with correlated samples

International Nuclear Information System (INIS)

Obermayer, Benedikt; Levine, Erel

2014-01-01

Correlations between two variables of a high-dimensional system can be indicative of an underlying interaction, but can also result from indirect effects. Inverse Ising inference is a method to distinguish one from the other. Essentially, the parameters of the least constrained statistical model are learned from the observed correlations such that direct interactions can be separated from indirect correlations. Among many other applications, this approach has been helpful for protein structure prediction, because residues which interact in the 3D structure often show correlated substitutions in a multiple sequence alignment. In this context, samples used for inference are not independent but share an evolutionary history on a phylogenetic tree. Here, we discuss the effects of correlations between samples on global inference. Such correlations could arise due to phylogeny but also via other slow dynamical processes. We present a simple analytical model to address the resulting inference biases, and develop an exact method accounting for background correlations in alignment data by combining phylogenetic modeling with an adaptive cluster expansion algorithm. We find that popular reweighting schemes are only marginally effective at removing phylogenetic bias, suggest a rescaling strategy that yields better results, and provide evidence that our conclusions carry over to the frequently used mean-field approach to the inverse Ising problem. (paper)

Bayesian structural inference for hidden processes

Science.gov (United States)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

The Impact of Disablers on Predictive Inference

Science.gov (United States)

Cummins, Denise Dellarosa

2014-01-01

People consider alternative causes when deciding whether a cause is responsible for an effect (diagnostic inference) but appear to neglect them when deciding whether an effect will occur (predictive inference). Five experiments were conducted to test a 2-part explanation of this phenomenon: namely, (a) that people interpret standard predictive…

Automatic physical inference with information maximizing neural networks

Science.gov (United States)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.

Decarbonation in an intracratonic setting: Insight from petrological-thermomechanical modeling

Science.gov (United States)

Gonzalez, Christopher M.; Gorczyk, Weronika

2017-08-01

Cratons form the stable core roots of the continental crust. Despite long-term stability, cratons have failed in the past. Cratonic destruction (e.g., North Atlantic Craton) due to chemical rejuvenation at the base of the lithosphere remains poorly constrained numerically. We use 2-D petrological-thermomechanical models to assess cratonic rifting characteristics and mantle CO2 degassing in the presence of a carbonated subcontinental lithospheric mantle (SCLM). We test two tectonothermal SCLM compositions: Archon (depleted) and Tecton (fertilized) using 2 CO2 wt % in the bulk composition to represent a metasomatized SCLM. We parameterize cratonic breakup via extensional duration (7-12 Ma; full breakup), tectonothermal age, TMoho (300-600°C), and crustal rheology. The two compositions with metasomatized SCLMs share similar rifting features and decarbonation trends during initial extension. However, we show long-term (>67 Ma) stability differences due to lithospheric density contrasts between SCLM compositions. The Tecton model shows convective removal and thinning of the metasomatized SCLM during failed rifting. The Archon composition remained stable, highlighting the primary role for SCLM density even when metasomatized at its base. In the short-term, three failed rifting characteristics emerge: failed rifting without decarbonation, failed rifting with decarbonation, and semifailed rifting with dry asthenospheric melting and decarbonation. Decarbonation trends were greatest in the failed rifts, reaching peak fluxes of 94 × 104 kg m-3. Increased TMoho did not alter the effects of rifting or decarbonation. Lastly, we show mantle regions where decarbonation, mantle melting in the presence of carbonate, and preservation of carbonated mantle occur during rifting.

Petrologic insights into basaltic volcanism at historically active Hawaiian volcanoes: Chapter 6 in Characteristics of Hawaiian volcanoes

Science.gov (United States)

Helz, Rosalind L.; Clague, David A.; Sisson, Thomas W.; Thornber, Carl R.; Poland, Michael P.; Takahashi, T. Jane; Landowski, Claire M.

2014-01-01

Study of the petrology of Hawaiian volcanoes, in particular the historically active volcanoes on the Island of Hawai‘i, has long been of worldwide scientific interest. When Dr. Thomas A. Jaggar, Jr., established the Hawaiian Volcano Observatory (HVO) in 1912, detailed observations on basaltic activity at Kīlauea and Mauna Loa volcanoes increased dramatically. The period from 1912 to 1958 saw a gradual increase in the collection and analysis of samples from the historical eruptions of Kīlauea and Mauna Loa and development of the concepts needed to evaluate them. In a classic 1955 paper, Howard Powers introduced the concepts of magnesia variation diagrams, to display basaltic compositions, and olivine-control lines, to distinguish between possibly comagmatic and clearly distinct basaltic lineages. In particular, he and others recognized that Kīlauea and Mauna Loa basalts must have different sources.

Preliminary geologic map of the Sleeping Butte volcanic centers

International Nuclear Information System (INIS)

Crowe, B.M.; Perry, F.V.

1991-07-01

The Sleeping Butte volcanic centers comprise two, spatially separate, small-volume ( 3 ) basaltic centers. The centers were formed by mildly explosive Strombolian eruptions. The Little Black Peak cone consists of a main scoria cone, two small satellitic scoria mounds, and associated lobate lava flows that vented from sites at the base of the scoria cone. The Hidden Cone center consists of a main scoria cone that developed on the north-facing slope of Sleeping Butte. The center formed during two episodes. The first included the formation of the main scoria cone, and venting of aa lava flows from radial dikes at the northeast base of the cone. The second included eruption of scoria-fall deposits from the summit crater. The ages of the Little Black Peak and the Hidden Cone are estimated to be between 200 to 400 ka based on the whole-rock K-Ar age determinations with large analytical undertainty. This age assignment is consistent with qualitative observations of the degree of soil development and geomorphic degradation of volcanic landforms. The younger episode of the Hidden Cone is inferred to be significantly younger and probably of Late Pleistocene or Holocene age. This is based on the absence of cone slope rilling, the absence of cone-slope apron deposits, and erosional unconformity between the two episodes, the poor horizon- development of soils, and the presence of fall deposits on modern alluvial surfaces. Paleomagnetic data show that the centers record similar but not identical directions of remanent magnetization. Paleomagnetic data have not been obtained for the youngest deposits of the Hidden Cone center. Further geochronology, soils, geomorphic, and petrology studies are planned of the Sleeping Butte volcanic centers 20 refs., 3 figs

Inference as Prediction

Science.gov (United States)

Watson, Jane

2007-01-01

Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…

Problem solving and inference mechanisms

Energy Technology Data Exchange (ETDEWEB)

Furukawa, K; Nakajima, R; Yonezawa, A; Goto, S; Aoyama, A

1982-01-01

The heart of the fifth generation computer will be powerful mechanisms for problem solving and inference. A deduction-oriented language is to be designed, which will form the core of the whole computing system. The language is based on predicate logic with the extended features of structuring facilities, meta structures and relational data base interfaces. Parallel computation mechanisms and specialized hardware architectures are being investigated to make possible efficient realization of the language features. The project includes research into an intelligent programming system, a knowledge representation language and system, and a meta inference system to be built on the core. 30 references.

Elements of Causal Inference: Foundations and Learning Algorithms

DEFF Research Database (Denmark)

Peters, Jonas Martin; Janzing, Dominik; Schölkopf, Bernhard

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning......A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning...

Preliminary Iron Distribution on Vesta

Science.gov (United States)

Mittlefehldt, David W.; Mittlefehldt, David W.

2013-01-01

The distribution of iron on the surface of the asteroid Vesta was investigated using Dawn's Gamma Ray and Neutron Detector (GRaND) [1,2]. Iron varies predictably with rock type for the howardite, eucrite, and diogenite (HED) meteorites, thought to be representative of Vesta. The abundance of Fe in howardites ranges from about 12 to 15 wt.%. Basaltic eucrites have the highest abundance, whereas, lower crustal and upper mantle materials (cumulate eucrites and diogenites) have the lowest, and howardites are intermediate [3]. We have completed a mapping study of 7.6 MeV gamma rays produced by neutron capture by Fe as measured by the bismuth germanate (BGO) detector of GRaND [1]. The procedures to determine Fe counting rates are presented in detail here, along with a preliminary distribution map, constituting the necessary initial step to quantification of Fe abundances. We find that the global distribution of Fe counting rates is generally consistent with independent mineralogical and compositional inferences obtained by other instruments on Dawn such as measurements of pyroxene absorption bands by the Visual and Infrared Spectrometer (VIR) [4] and Framing Camera (FC) [5] and neutron absorption measurements by GRaND [6].

Bayesian methods for hackers probabilistic programming and Bayesian inference

CERN Document Server

Davidson-Pilon, Cameron

2016-01-01

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...

Preliminary integration study of Precambrian with tectonic events in Brazilian sedimentary basins (Republication); Estudo preliminar de integracao do Pre-Cambriano com os eventos tectonicos das bacias sedimentares brasileiras (Republicacao)

Energy Technology Data Exchange (ETDEWEB)

Cordani, Umberto G.; Neves, Benjamim B. Brito; Fuck, Reinhard A.; Porto, Roberto; Thomaz Filho, Antonio; Cunha, Francisco M. Bezerra da

2008-11-15

The various successive episodes of vertical cratogenic evolution modelling the geo tectonic features of the basement were correlated with the internal structure, shape, origin and geologic evolution of the sedimentary basin. A systematic petrologic and geochronological investigation of all available drill core samples was carried out, and the pertinent geophysical data regarding basement structure were taken into consideration. Specific geo tectonic analyses, were carried out along the borders of the sedimentary basins, within the adjacent basement. The main boundaries between Precambrian tectonic provinces, the main tectonic sutures with polycyclic evolution, and the ancient intracratonic rifts were identified wherever possible. Their extensions under the sedimentary basins were inferred, corroboration being sought from structural information and data obtained from the drill core samples. It was found that many of the identified basement discontinuities had a direct influence on the depositional history of each of the sedimentary basins, demonstrating the distinct tectonic inheritance. The subject was treated on a reconnaissance scale, 1:1.000.000 or smaller,owing to its complexity. (author)

Estimation of Water Within the Lithospheric Mantle of Central Tibet from Petrological-Geophysical Investigations

Science.gov (United States)

Vozar, J.; Fullea, J.; Jones, A. G.

2013-12-01

Investigations of the lithosphere and sub-lithospheric upper mantle by integrated petrological-geophysical modeling of magnetotelluric (MT) and seismic surface-wave data, which are differently sensitive to temperature and composition, allows us to reduce the uncertainties associated with modeling these two data sets independently, as commonly undertaken. We use selected INDEPTH MT data, which have appropriate dimensionality and large penetration depths, across central Tibet for 1D modeling. Our deep resistivity models from the data can be classified into two different and distinct groups: (i) the Lhasa Terrane and (ii) the Qiangtang Terrane. For the Lhasa Terrane group, the models show the existence of upper mantle conductive layer localized at depths of 200 km, whereas for the Qiangtang Terrane, this conductive layer is shallower at depths of 120 km. We perform the integrated geophysical-petrological modeling of the MT and surface-wave data using the software package LitMod. The program facilitates definition of realistic temperature and pressure distributions within the upper mantle for given thermal structure and oxide chemistry in the CFMAS system. This allows us to define a bulk geoelectric and seismic model of the upper mantle based on laboratory and xenolith data for the most relevant mantle minerals, and to compute synthetic geophysical observables. Our results suggest an 80-120 km-thick, dry lithosphere in the central part of the Qiangtang Terrane. In contrast, in the central Lhasa Terrane the predicted MT responses are too resistive for a dry lithosphere regardless its thickness; according to seismic and topography data the expected lithospheric thickness is about 200 km. The presence of small amounts of water significantly decreases the electrical resistivity of mantle rocks and is required to fit the MT responses. We test the hypothesis of small amounts of water (ppm scale) in the nominally anhydrous minerals of the lithospheric mantle. Such a small

Causal inference in econometrics

CERN Document Server

Kreinovich, Vladik; Sriboonchitta, Songsak

2016-01-01

This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

Petrological cycles and caldera-forming events

Science.gov (United States)

Bachmann, O.; Deering, C. D.

2012-12-01

Many caldera-forming events can be framed within broad petrological cycles; volcanic stratigraphy typically defines a trend from mafic to more silicic magmas with time, culminating in the catastrophic evacuation of an upper crustal reservoir filled with the silicic magma, followed by a return to the eruption of more mafic magmas shortly after caldera collapse. Understanding how such cycles develop has clear implications for characterizing the current state of an active system. Here, we focus on a detailed examination of the well-exposed Quaternary Kos-Nisyros eruptive sequence (eastern Aegean arc) to frame a potential model for such cycles. On the basis of zircon U/Th/Pb ages, building the upper crustal magma chamber large enough to induce caldera collapse required at least a few hundred thousand years. This timeframe is necessary not only for the accumulation of large amounts of viscous, gas-rich silicic magma, but also to heat the upper crust sufficiently to allow the developing reservoir to be maintained above the solidus. In the Kos-Nisyros volcanic center, small eruptions precede the caldera-forming event and mark this period of thermal maturation as the system transitions from intermediate to silicic magma, reaching the most-evolved state only shortly prior to the caldera-forming event, the Kos Plateau Tuff (> 60 km3 of volatile-rich, high-silica rhyolite). The Kos Plateau Tuff was then followed by small-volume eruptions of more mafic magma (basaltic andesite, andesite, and dacites) that are characterized by a drier mineral assemblage. With time, the system transitioned back to cold, wet, high-SiO2 rhyolite. We suggest that the changes in magma composition and mineralogy following the caldera-forming event are due to a near-complete crystallization of the non-erupted mush in the upper crustal reservoir as it is abruptly decompressed during eruption. This rapid crystallization (1) leads to the formation of a porphyritic texture in the crystalline residual - a

Assessment of network inference methods: how to cope with an underdetermined problem.

Directory of Open Access Journals (Sweden)

Caroline Siegenthaler

Full Text Available The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.

Probability and Statistical Inference

OpenAIRE

Prosper, Harrison B.

2006-01-01

These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.

Fuzzy logic controller using different inference methods

International Nuclear Information System (INIS)

Liu, Z.; De Keyser, R.

1994-01-01

In this paper the design of fuzzy controllers by using different inference methods is introduced. Configuration of the fuzzy controllers includes a general rule-base which is a collection of fuzzy PI or PD rules, the triangular fuzzy data model and a centre of gravity defuzzification algorithm. The generalized modus ponens (GMP) is used with the minimum operator of the triangular norm. Under the sup-min inference rule, six fuzzy implication operators are employed to calculate the fuzzy look-up tables for each rule base. The performance is tested in simulated systems with MATLAB/SIMULINK. Results show the effects of using the fuzzy controllers with different inference methods and applied to different test processes

An algebra-based method for inferring gene regulatory networks.

Science.gov (United States)

Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

2014-03-26

The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

Statistical inference based on divergence measures

CERN Document Server

Pardo, Leandro

2005-01-01

The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, prese...

Active inference, sensory attenuation and illusions.

Science.gov (United States)

Brown, Harriet; Adams, Rick A; Parees, Isabel; Edwards, Mark; Friston, Karl

2013-11-01

Active inference provides a simple and neurobiologically plausible account of how action and perception are coupled in producing (Bayes) optimal behaviour. This can be seen most easily as minimising prediction error: we can either change our predictions to explain sensory input through perception. Alternatively, we can actively change sensory input to fulfil our predictions. In active inference, this action is mediated by classical reflex arcs that minimise proprioceptive prediction error created by descending proprioceptive predictions. However, this creates a conflict between action and perception; in that, self-generated movements require predictions to override the sensory evidence that one is not actually moving. However, ignoring sensory evidence means that externally generated sensations will not be perceived. Conversely, attending to (proprioceptive and somatosensory) sensations enables the detection of externally generated events but precludes generation of actions. This conflict can be resolved by attenuating the precision of sensory evidence during movement or, equivalently, attending away from the consequences of self-made acts. We propose that this Bayes optimal withdrawal of precise sensory evidence during movement is the cause of psychophysical sensory attenuation. Furthermore, it explains the force-matching illusion and reproduces empirical results almost exactly. Finally, if attenuation is removed, the force-matching illusion disappears and false (delusional) inferences about agency emerge. This is important, given the negative correlation between sensory attenuation and delusional beliefs in normal subjects--and the reduction in the magnitude of the illusion in schizophrenia. Active inference therefore links the neuromodulatory optimisation of precision to sensory attenuation and illusory phenomena during the attribution of agency in normal subjects. It also provides a functional account of deficits in syndromes characterised by false inference

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

Science.gov (United States)

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Contingency inferences driven by base rates: Valid by sampling

Directory of Open Access Journals (Sweden)

Florian Kutzner

2011-04-01

Full Text Available Fiedler et al. (2009, reviewed evidence for the utilization of a contingency inference strategy termed pseudocontingencies (PCs. In PCs, the more frequent levels (and, by implication, the less frequent levels are assumed to be associated. PCs have been obtained using a wide range of task settings and dependent measures. Yet, the readiness with which decision makers rely on PCs is poorly understood. A computer simulation explored two potential sources of subjective validity of PCs. First, PCs are shown to perform above chance level when the task is to infer the sign of moderate to strong population contingencies from a sample of observations. Second, contingency inferences based on PCs and inferences based on cell frequencies are shown to partially agree across samples. Intriguingly, this criterion and convergent validity are by-products of random sampling error, highlighting the inductive nature of contingency inferences.

Reinforcement and inference in cross-situational word learning.

Science.gov (United States)

Tilles, Paulo F C; Fontanari, José F

2013-01-01

Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.

Data-driven inference for the spatial scan statistic.

Science.gov (United States)

Almeida, Alexandre C L; Duarte, Anderson R; Duczmal, Luiz H; Oliveira, Fernando L P; Takahashi, Ricardo H C

2011-08-02

Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

Preliminary prediction of inflow into the D-holes at the Stripa Mine

International Nuclear Information System (INIS)

Long, J.C.S.; Karasaki, K.; Davey, A.; Peterson, J.; Landsfeld, M.; Kemeny, J.; Martel, S.

1990-02-01

Lawrence Berkeley Laboratory (LBL) is contracted by the US Department of Energy to provide an auxiliary modeling effort for the Stripa Project. Within this effort, we are making calculations of inflow to the Simulated Drift Experiment (SDE), i.e. inflow to six parallel, closely spaced D-holes, using a preliminary set of data collected in five other holes, the N- and W-holes during Stages 1 and 2 of the Site Characterization and Validation (SCV) project. Our approach has been to focus on the fracture zones rather than the general set of ubiquitous fractures. Approximately 90% of all the water flowing in the rock is flowing in fracture zones which are neither uniformly conductive nor are they infinitely extensive. Our approach has been to adopt the fracture zone locations as they have been identified with geophysics. We use geologic sense and the original geophysical data to add one zone where significant water inflow has been observed that can not be explained with the other geophysical zones. This report covers LBL's preliminary prediction of flow into the D-holes. Care should be taken in interpreting the results given in this report. As explained below, the approach that LBL has designed for developing a fracture hydrology model requires cross-hole hydrologic data. Cross-hole tests are planned for Stage 3 but were unavailable in Stage 1. As such, we have inferred from available data what a cross-hole test might show and used this synthetic data to make a preliminary calculation of the inflow into the D-holes. Then using all the Stage 3 data we will calculate flow into the Validation Drift itself. The report mainly demonstrates the use of our methodology and the simulated results should be considered preliminary

Eight challenges in phylodynamic inference

Directory of Open Access Journals (Sweden)

Simon D.W. Frost

2015-03-01

Full Text Available The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.

Human Inferences about Sequences: A Minimal Transition Probability Model.

Directory of Open Access Journals (Sweden)

Florent Meyniel

2016-12-01

Full Text Available The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.

Making Type Inference Practical

DEFF Research Database (Denmark)

Schwartzbach, Michael Ignatieff; Oxhøj, Nicholas; Palsberg, Jens

1992-01-01

We present the implementation of a type inference algorithm for untyped object-oriented programs with inheritance, assignments, and late binding. The algorithm significantly improves our previous one, presented at OOPSLA'91, since it can handle collection classes, such as List, in a useful way. Abo......, the complexity has been dramatically improved, from exponential time to low polynomial time. The implementation uses the techniques of incremental graph construction and constraint template instantiation to avoid representing intermediate results, doing superfluous work, and recomputing type information....... Experiments indicate that the implementation type checks as much as 100 lines pr. second. This results in a mature product, on which a number of tools can be based, for example a safety tool, an image compression tool, a code optimization tool, and an annotation tool. This may make type inference for object...

Examples in parametric inference with R

CERN Document Server

Dixit, Ulhas Jayram

2016-01-01

This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory cou...

Causal Effect Inference with Deep Latent-Variable Models

NARCIS (Netherlands)

Louizos, C; Shalit, U.; Mooij, J.; Sontag, D.; Zemel, R.; Welling, M.

2017-01-01

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of

Causal inference in survival analysis using pseudo-observations

DEFF Research Database (Denmark)

Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T

2017-01-01

Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs ...

Statistical Inference at Work: Statistical Process Control as an Example

Science.gov (United States)

Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia

2008-01-01

To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…

On quantum statistical inference

NARCIS (Netherlands)

Barndorff-Nielsen, O.E.; Gill, R.D.; Jupp, P.E.

2003-01-01

Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, developments in the theory of quantum measurements have

Statistical inference

CERN Document Server

Rohatgi, Vijay K

2003-01-01

Unified treatment of probability and statistics examines and analyzes the relationship between the two fields, exploring inferential issues. Numerous problems, examples, and diagrams--some with solutions--plus clear-cut, highlighted summaries of results. Advanced undergraduate to graduate level. Contents: 1. Introduction. 2. Probability Model. 3. Probability Distributions. 4. Introduction to Statistical Inference. 5. More on Mathematical Expectation. 6. Some Discrete Models. 7. Some Continuous Models. 8. Functions of Random Variables and Random Vectors. 9. Large-Sample Theory. 10. General Meth

The Impact of Contextual Clue Selection on Inference

Directory of Open Access Journals (Sweden)

Leila Barati

2010-05-01

Full Text Available Linguistic information can be conveyed in the form of speech and written text, but it is the content of the message that is ultimately essential for higher-level processes in language comprehension, such as making inferences and associations between text information and knowledge about the world. Linguistically, inference is the shovel that allows receivers to dig meaning out from the text with selecting different embedded contextual clues. Naturally, people with different world experiences infer similar contextual situations differently. Lack of contextual knowledge of the target language can present an obstacle to comprehension (Anderson & Lynch, 2003. This paper tries to investigate how true contextual clue selection from the text can influence listener’s inference. In the present study 60 male and female teenagers (13-19 and 60 male and female young adults (20-26 were selected randomly based on Oxford Placement Test (OPT. During the study two fiction and two non-fiction passages were read to the participants in the experimental and control groups respectively and they were given scores according to Lexile’s Score (LS[1] based on their correct inference and logical thinking ability. In general the results show that participants’ clue selection based on their personal schematic references and background knowledge differ between teenagers and young adults and influence inference and listening comprehension. [1]- This is a framework for reading and listening which matches the appropriate score to each text based on degree of difficulty of text and each text was given a Lexile score from zero to four.

Inferring Demographic History Using Two-Locus Statistics.

Science.gov (United States)

Ragsdale, Aaron P; Gutenkunst, Ryan N

2017-06-01

Population demographic history may be learned from contemporary genetic variation data. Methods based on aggregating the statistics of many single loci into an allele frequency spectrum (AFS) have proven powerful, but such methods ignore potentially informative patterns of linkage disequilibrium (LD) between neighboring loci. To leverage such patterns, we developed a composite-likelihood framework for inferring demographic history from aggregated statistics of pairs of loci. Using this framework, we show that two-locus statistics are more sensitive to demographic history than single-locus statistics such as the AFS. In particular, two-locus statistics escape the notorious confounding of depth and duration of a bottleneck, and they provide a means to estimate effective population size based on the recombination rather than mutation rate. We applied our approach to a Zambian population of Drosophila melanogaster Notably, using both single- and two-locus statistics, we inferred a substantially lower ancestral effective population size than previous works and did not infer a bottleneck history. Together, our results demonstrate the broad potential for two-locus statistics to enable powerful population genetic inference. Copyright © 2017 by the Genetics Society of America.

Statistical Inference on the Canadian Middle Class

Directory of Open Access Journals (Sweden)

Russell Davidson

2018-03-01

Full Text Available Conventional wisdom says that the middle classes in many developed countries have recently suffered losses, in terms of both the share of the total population belonging to the middle class, and also their share in total income. Here, distribution-free methods are developed for inference on these shares, by means of deriving expressions for their asymptotic variances of sample estimates, and the covariance of the estimates. Asymptotic inference can be undertaken based on asymptotic normality. Bootstrap inference can be expected to be more reliable, and appropriate bootstrap procedures are proposed. As an illustration, samples of individual earnings drawn from Canadian census data are used to test various hypotheses about the middle-class shares, and confidence intervals for them are computed. It is found that, for the earlier censuses, sample sizes are large enough for asymptotic and bootstrap inference to be almost identical, but that, in the twenty-first century, the bootstrap fails on account of a strange phenomenon whereby many presumably different incomes in the data are rounded to one and the same value. Another difference between the centuries is the appearance of heavy right-hand tails in the income distributions of both men and women.

The importance of learning when making inferences

Directory of Open Access Journals (Sweden)

Jorg Rieskamp

2008-03-01

Full Text Available The assumption that people possess a repertoire of strategies to solve the inference problems they face has been made repeatedly. The experimental findings of two previous studies on strategy selection are reexamined from a learning perspective, which argues that people learn to select strategies for making probabilistic inferences. This learning process is modeled with the strategy selection learning (SSL theory, which assumes that people develop subjective expectancies for the strategies they have. They select strategies proportional to their expectancies, which are updated on the basis of experience. For the study by Newell, Weston, and Shanks (2003 it can be shown that people did not anticipate the success of a strategy from the beginning of the experiment. Instead, the behavior observed at the end of the experiment was the result of a learning process that can be described by the SSL theory. For the second study, by Br"oder and Schiffer (2006, the SSL theory is able to provide an explanation for why participants only slowly adapted to new environments in a dynamic inference situation. The reanalysis of the previous studies illustrates the importance of learning for probabilistic inferences.

Bayesian inference of substrate properties from film behavior

International Nuclear Information System (INIS)

Aggarwal, R; Demkowicz, M J; Marzouk, Y M

2015-01-01

We demonstrate that by observing the behavior of a film deposited on a substrate, certain features of the substrate may be inferred with quantified uncertainty using Bayesian methods. We carry out this demonstration on an illustrative film/substrate model where the substrate is a Gaussian random field and the film is a two-component mixture that obeys the Cahn–Hilliard equation. We construct a stochastic reduced order model to describe the film/substrate interaction and use it to infer substrate properties from film behavior. This quantitative inference strategy may be adapted to other film/substrate systems. (paper)

Brain Imaging, Forward Inference, and Theories of Reasoning

Science.gov (United States)

Heit, Evan

2015-01-01

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities. PMID:25620926

Brain imaging, forward inference, and theories of reasoning.

Science.gov (United States)

Heit, Evan

2014-01-01

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities.

Data-driven inference for the spatial scan statistic

Directory of Open Access Journals (Sweden)

Duczmal Luiz H

2011-08-01

Full Text Available Abstract Background Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. Results A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. Conclusions A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

Petrology of the Devonian gas-bearing shale along Lake Erie helps explain gas shows

Energy Technology Data Exchange (ETDEWEB)

Broadhead, R.F.; Potter, P.E.

1980-11-01

Comprehensive petrologic study of 136 thin sections of the Ohio Shale along Lake Erie, when combined with detailed stratigraphic study, helps explain the occurrence of its gas shows, most of which occur in the silty, greenish-gray, organic poor Chagrin Shale and Three Lick Bed. Both have thicker siltstone laminae and more siltstone beds than other members of the Ohio Shale and both units also contain more clayshales. The source of the gas in the Chagrin Shale and Three Lick Bed of the Ohio Shale is believed to be the bituminous-rich shales of the middle and lower parts of the underlying Huron Member of the Ohio Shale. Eleven petrographic types were recognized and extended descriptions are provided of the major ones - claystones, clayshales, mudshales, and bituminous shales plus laminated and unlaminated siltstones and very minor marlstones and sandstones. In addition three major types of lamination were identified and studied. Thirty-two shale samples were analyzed for organic carbon, whole rock hydrogen and whole rock nitrogen with a Perkin-Elmer 240 Elemental Analyzer and provided the data base for source rock evaluation of the Ohio Shale.

Statistical inference an integrated approach

CERN Document Server

Migon, Helio S; Louzada, Francisco

2014-01-01

Introduction Information The concept of probability Assessing subjective probabilities An example Linear algebra and probability Notation Outline of the bookElements of Inference Common statistical modelsLikelihood-based functions Bayes theorem Exchangeability Sufficiency and exponential family Parameter elimination Prior Distribution Entirely subjective specification Specification through functional forms Conjugacy with the exponential family Non-informative priors Hierarchical priors Estimation Introduction to decision theoryBayesian point estimation Classical point estimation Empirical Bayes estimation Comparison of estimators Interval estimation Estimation in the Normal model Approximating Methods The general problem of inference Optimization techniquesAsymptotic theory Other analytical approximations Numerical integration methods Simulation methods Hypothesis Testing Introduction Classical hypothesis testingBayesian hypothesis testing Hypothesis testing and confidence intervalsAsymptotic tests Prediction...

Statistical learning and selective inference.

Science.gov (United States)

Taylor, Jonathan; Tibshirani, Robert J

2015-06-23

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

Object-Oriented Type Inference

DEFF Research Database (Denmark)

Schwartzbach, Michael Ignatieff; Palsberg, Jens

1991-01-01

We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an op...

The Probabilistic Convolution Tree: Efficient Exact Bayesian Inference for Faster LC-MS/MS Protein Inference

Science.gov (United States)

Serang, Oliver

2014-01-01

Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called “causal independence”). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to and the space to where is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions. PMID:24626234

Reward inference by primate prefrontal and striatal neurons.

Science.gov (United States)

Pan, Xiaochuan; Fan, Hongwei; Sawa, Kosuke; Tsuda, Ichiro; Tsukada, Minoru; Sakagami, Masamichi

2014-01-22

The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Importantly, these LPFC neurons could predict the reward value of a stimulus using transitive inference even when the monkeys had not yet learned the stimulus-reward association directly; whereas these striatal neurons did not show such an ability. Nevertheless, because there were two set amounts of reward (large and small), the selected striatal neurons were able to exclusively infer the reward value (e.g., large) of one novel stimulus from a pair after directly experiencing the alternative stimulus with the other reward value (e.g., small). Our results suggest that although neurons that predict reward value for old stimuli in the LPFC could also do so for new stimuli via transitive inference, those in the striatum could only predict reward for new stimuli via exclusive inference. Moreover, the striatum showed more complex functions than was surmised previously for model-free learning.

Bootstrap inference when using multiple imputation.

Science.gov (United States)

Schomaker, Michael; Heumann, Christian

2018-04-16

Many modern estimators require bootstrapping to calculate confidence intervals because either no analytic standard error is available or the distribution of the parameter of interest is nonsymmetric. It remains however unclear how to obtain valid bootstrap inference when dealing with multiple imputation to address missing data. We present 4 methods that are intuitively appealing, easy to implement, and combine bootstrap estimation with multiple imputation. We show that 3 of the 4 approaches yield valid inference, but that the performance of the methods varies with respect to the number of imputed data sets and the extent of missingness. Simulation studies reveal the behavior of our approaches in finite samples. A topical analysis from HIV treatment research, which determines the optimal timing of antiretroviral treatment initiation in young children, demonstrates the practical implications of the 4 methods in a sophisticated and realistic setting. This analysis suffers from missing data and uses the g-formula for inference, a method for which no standard errors are available. Copyright © 2018 John Wiley & Sons, Ltd.

Evolutionary inference via the Poisson Indel Process.

Science.gov (United States)

Bouchard-Côté, Alexandre; Jordan, Michael I

2013-01-22

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.

System Support for Forensic Inference

Science.gov (United States)

Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan

Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.

Geostatistical inference using crosshole ground-penetrating radar

DEFF Research Database (Denmark)

Looms, Majken C; Hansen, Thomas Mejer; Cordua, Knud Skou

2010-01-01

of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance...... reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisturecontent measurements, obtained gravimetrically from samples collected at the field site....

Dinoflagellate phylogeny as inferred from heat shock protein 90 and ribosomal gene sequences.

Directory of Open Access Journals (Sweden)

Mona Hoppenrath

2010-10-01

Full Text Available Interrelationships among dinoflagellates in molecular phylogenies are largely unresolved, especially in the deepest branches. Ribosomal DNA (rDNA sequences provide phylogenetic signals only at the tips of the dinoflagellate tree. Two reasons for the poor resolution of deep dinoflagellate relationships using rDNA sequences are (1 most sites are relatively conserved and (2 there are different evolutionary rates among sites in different lineages. Therefore, alternative molecular markers are required to address the deeper phylogenetic relationships among dinoflagellates. Preliminary evidence indicates that the heat shock protein 90 gene (Hsp90 will provide an informative marker, mainly because this gene is relatively long and appears to have relatively uniform rates of evolution in different lineages.We more than doubled the previous dataset of Hsp90 sequences from dinoflagellates by generating additional sequences from 17 different species, representing seven different orders. In order to concatenate the Hsp90 data with rDNA sequences, we supplemented the Hsp90 sequences with three new SSU rDNA sequences and five new LSU rDNA sequences. The new Hsp90 sequences were generated, in part, from four additional heterotrophic dinoflagellates and the type species for six different genera. Molecular phylogenetic analyses resulted in a paraphyletic assemblage near the base of the dinoflagellate tree consisting of only athecate species. However, Noctiluca was never part of this assemblage and branched in a position that was nested within other lineages of dinokaryotes. The phylogenetic trees inferred from Hsp90 sequences were consistent with trees inferred from rDNA sequences in that the backbone of the dinoflagellate clade was largely unresolved.The sequence conservation in both Hsp90 and rDNA sequences and the poor resolution of the deepest nodes suggests that dinoflagellates reflect an explosive radiation in morphological diversity in their recent

Bayesian Inference for Functional Dynamics Exploring in fMRI Data

Directory of Open Access Journals (Sweden)

Xuan Guo

2016-01-01

Full Text Available This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM, Bayesian Connectivity Change Point Model (BCCPM, and Dynamic Bayesian Variable Partition Model (DBVPM, and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

Ground Field-Based Hyperspectral Imaging: A Preliminary Study to Assess the Potential of Established Vegetation Indices to Infer Variation in Water-Use Efficiency.

Science.gov (United States)

Pelech, E. A.; McGrath, J.; Pederson, T.; Bernacchi, C.

2017-12-01

Increases in the global average temperature will consequently induce a higher occurrence of severe environmental conditions such as drought on arable land. To mitigate these threats, crops for fuel and food must be bred for higher water-use efficiencies (WUE). Defining genomic variation through high-throughput phenotypic analysis in field conditions has the potential to relieve the major bottleneck in linking desirable genetic traits to the associated phenotypic response. This can subsequently enable breeders to create new agricultural germplasm that supports the need for higher water-use efficient crops. From satellites to field-based aerial and ground sensors, the reflectance properties of vegetation measured by hyperspectral imaging is becoming a rapid high-throughput phenotyping technique. A variety of physiological traits can be inferred by regression analysis with leaf reflectance which is controlled by the properties and abundance of water, carbon, nitrogen and pigments. Although, given that the current established vegetation indices are designed to accentuate these properties from spectral reflectance, it becomes a challenge to infer relative measurements of WUE at a crop canopy scale without ground-truth data collection. This study aims to correlate established biomass and canopy-water-content indices with ground-truth data. Five bioenergy sorghum genotypes (Sorghum bicolor L. Moench) that have differences in WUE and wild-type Tobacco (Nicotiana tabacum var. Samsun) under irrigated and rainfed field conditions were examined. A linear regression analysis was conducted to determine if variation in canopy water content and biomass, driven by natural genotypic and artificial treatment influences, can be inferred using established vegetation indices. The results from this study will elucidate the ability of ground field-based hyperspectral imaging to assess variation in water content, biomass and water-use efficiency. This can lead to improved opportunities to

Working memory supports inference learning just like classification learning.

Science.gov (United States)

Craig, Stewart; Lewandowsky, Stephan

2013-08-01

Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.

Petrology of Terra Nova pluton, Brazil, and associated ultrapotassic dykes

International Nuclear Information System (INIS)

Silva Filho, A.F. da; Thompson, R.N.; Leat, P.T.

1987-01-01

The Upper Precambrian Terra Nova Pluton, situated 550 Km inland from Recife, Brazil, is 220 Km 2 in area and intrudes deformed metasedimentary rocks of the Pianco-Alto Brigida Mobile Belt. The Pluton shows complex petrological relationships. It consists of subalkaline quartz-monzonites and quartz-syenites, and the major minerals are K-feldspars, albite, hornblende, and quartz. The pluton is intermediate in composition (SiO 2 = 58.9-65.6 wt%, MgO=0.9-3.7 wt%) and is dominantly potassic (K 2 O=3.3-5.6 wt %; K 2 O/Na 2 O=0.9-1.8). Ba (up to 2.300 ppm) and Sr (up to 1,100 ppm) are abundant in the rocks, and LREE are enriched relative to HREE (La N /Lu N = 25.6-43.2). There is no significant Eu Anomaly. Rounded autoliths within the pluton are similar, but more mafic in composition (SiO 2 =54.6-57.5 wt %; MgO=4.9-6.4 wt %). A suite of dykes cut pluton and the surrounding country rocks. These dykes are varied in composition, encompassing most of the chemical range shown by the pluton and associated autoliths. The dykes are holocrystalline, peralkaline, and strongly enriched in both K 2 O(K 2 O=5.3-11.4 wt %) and Ba (Ba=2,400 ppm-10,500 ppm), which are considered to be magmatic abundances. The dykes have similar REE and other trace elements and ratios to the autoliths and plutonic rocks, and the dykes and the pluton are thought to be chemically related. The Terra Nova Pluton records the fractionation of mantle-derived ultrapotassic magma from mafic to intermediate compositions. (author) [pt

Statistical inference for stochastic processes

National Research Council Canada - National Science Library

Basawa, Ishwar V; Prakasa Rao, B. L. S

1980-01-01

The aim of this monograph is to attempt to reduce the gap between theory and applications in the area of stochastic modelling, by directing the interest of future researchers to the inference aspects...

Inference of Large Phylogenies Using Neighbour-Joining

DEFF Research Database (Denmark)

Simonsen, Martin; Mailund, Thomas; Pedersen, Christian Nørgaard Storm

2011-01-01

The neighbour-joining method is a widely used method for phylogenetic reconstruction which scales to thousands of taxa. However, advances in sequencing technology have made data sets with more than 10,000 related taxa widely available. Inference of such large phylogenies takes hours or days using...... the Neighbour-Joining method on a normal desktop computer because of the O(n^3) running time. RapidNJ is a search heuristic which reduce the running time of the Neighbour-Joining method significantly but at the cost of an increased memory consumption making inference of large phylogenies infeasible. We present...... two extensions for RapidNJ which reduce the memory requirements and \\makebox{allows} phylogenies with more than 50,000 taxa to be inferred efficiently on a desktop computer. Furthermore, an improved version of the search heuristic is presented which reduces the running time of RapidNJ on many data...

Statistical causal inferences and their applications in public health research

CERN Document Server

Wu, Pan; Chen, Ding-Geng

2016-01-01

This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.

The anatomy of choice: active inference and agency

Directory of Open Access Journals (Sweden)

Karl eFriston

2013-09-01

Full Text Available This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behaviour. In particular, we consider prior beliefs that action minimises the Kullback-Leibler divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimises a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimising free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action – constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualises optimal decision theory and economic (utilitarian formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimisation, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria has a unique and Bayes-optimal solution – that minimises free energy. This sensitivity corresponds to the precision of beliefs about behaviour, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behaviour entails a representation of confidence about outcomes that are under an agent's control.

The anatomy of choice: active inference and agency.

Science.gov (United States)

Friston, Karl; Schwartenbeck, Philipp; Fitzgerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J

2013-01-01

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.

Universal Darwinism As a Process of Bayesian Inference.

Science.gov (United States)

Campbell, John O

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

sick: The Spectroscopic Inference Crank

Science.gov (United States)

Casey, Andrew R.

2016-03-01

There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal

SICK: THE SPECTROSCOPIC INFERENCE CRANK

Energy Technology Data Exchange (ETDEWEB)

Casey, Andrew R., E-mail: arc@ast.cam.ac.uk [Institute of Astronomy, University of Cambridge, Madingley Road, Cambdridge, CB3 0HA (United Kingdom)

2016-03-15

There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal

SICK: THE SPECTROSCOPIC INFERENCE CRANK

International Nuclear Information System (INIS)

Casey, Andrew R.

2016-01-01

There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal

Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

Directory of Open Access Journals (Sweden)

Nandkumar Wagh

2014-01-01

Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

On principles of inductive inference

OpenAIRE

Kostecki, Ryszard Paweł

2011-01-01

We propose an intersubjective epistemic approach to foundations of probability theory and statistical inference, based on relative entropy and category theory, and aimed to bypass the mathematical and conceptual problems of existing foundational approaches.

Geological and petrological considerations relevant to the disposal of radioactive wastes by hydraulic fracturing: an example at the US Department of Energy's Oak Ridge National Laboratory

International Nuclear Information System (INIS)

Haase, C.S.

1982-01-01

At Oak Ridge National Laboratory the Pumpkin Valley Shale is used as a host formation for hydraulic-fracturing waste disposal. Determination of the relationships between the distribution of different lithologies and porosity-permeability trends within this host formation allows these properties, important to hydraulic-fracturing operations, to be related to measurable and mappable geological and petrological parameters. It also permits extrapolation of such patterns to little-studied portions of the Pumpkin Valley Shale. Such knowledge better allows for the satisfactory operation and assessment of the hydraulic fracturing at Oak Ridge National Laboratory

Geological and petrological considerations relevant to the disposal of radioactive wastes by hydraulic fracturing: an example at the US Department of Energy's Oak Ridge National Laboratory

International Nuclear Information System (INIS)

Haase, C.S.

1983-01-01

At Oak Ridge National Laboratory the Pumpkin Valley Shale is used as a host formation for hydraulic fracturing waste disposal. Determination of the relationships between the distribution of different lithologies and porosity-permeability trends within this host formation allows these properties, important to hydraulic fracturing operations, to be related to measurable and mappable geological and petrological parameters. It also permits extrapolation of such patterns to little-studied portions of the Pumpkin Valley Shale. Such knowledge better allows for the satisfactory operation and assessment of the hydraulic fracturing at Oak Ridge National Laboratory

Model averaging, optimal inference and habit formation

Directory of Open Access Journals (Sweden)

Thomas H B FitzGerald

2014-06-01

Full Text Available Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge – that of determining which model or models of their environment are the best for guiding behaviour. Bayesian model averaging – which says that an agent should weight the predictions of different models according to their evidence – provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent’s behaviour should show an equivalent balance. We hypothesise that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realisable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behaviour. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded Bayesian inference, focussing particularly upon the relationship between goal-directed and habitual behaviour.

Bootstrapping phylogenies inferred from rearrangement data

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Lin Yu

2012-08-01

Full Text Available Abstract Background Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed over the last few years and have found use in comparative genomics and phylogenetic inference. However, the assessment of phylogenies built from such data has not been properly addressed to date. The standard method used in sequence-based phylogenetic inference is the bootstrap, but it relies on a large number of homologous characters that can be resampled; yet in the case of rearrangements, the entire genome is a single character. Alternatives such as the jackknife suffer from the same problem, while likelihood tests cannot be applied in the absence of well established probabilistic models. Results We present a new approach to the assessment of distance-based phylogenetic inference from whole-genome data; our approach combines features of the jackknife and the bootstrap and remains nonparametric. For each feature of our method, we give an equivalent feature in the sequence-based framework; we also present the results of extensive experimental testing, in both sequence-based and genome-based frameworks. Through the feature-by-feature comparison and the experimental results, we show that our bootstrapping approach is on par with the classic phylogenetic bootstrap used in sequence-based reconstruction, and we establish the clear superiority of the classic bootstrap for sequence data and of our corresponding new approach for rearrangement data over proposed variants. Finally, we test our approach on a small dataset of mammalian genomes, verifying that the support values match current thinking about the respective branches. Conclusions Our method is the first to provide a standard of assessment to match that of the classic phylogenetic bootstrap for aligned sequences. Its

Bootstrapping phylogenies inferred from rearrangement data.

Science.gov (United States)

Lin, Yu; Rajan, Vaibhav; Moret, Bernard Me

2012-08-29

Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed over the last few years and have found use in comparative genomics and phylogenetic inference. However, the assessment of phylogenies built from such data has not been properly addressed to date. The standard method used in sequence-based phylogenetic inference is the bootstrap, but it relies on a large number of homologous characters that can be resampled; yet in the case of rearrangements, the entire genome is a single character. Alternatives such as the jackknife suffer from the same problem, while likelihood tests cannot be applied in the absence of well established probabilistic models. We present a new approach to the assessment of distance-based phylogenetic inference from whole-genome data; our approach combines features of the jackknife and the bootstrap and remains nonparametric. For each feature of our method, we give an equivalent feature in the sequence-based framework; we also present the results of extensive experimental testing, in both sequence-based and genome-based frameworks. Through the feature-by-feature comparison and the experimental results, we show that our bootstrapping approach is on par with the classic phylogenetic bootstrap used in sequence-based reconstruction, and we establish the clear superiority of the classic bootstrap for sequence data and of our corresponding new approach for rearrangement data over proposed variants. Finally, we test our approach on a small dataset of mammalian genomes, verifying that the support values match current thinking about the respective branches. Our method is the first to provide a standard of assessment to match that of the classic phylogenetic bootstrap for aligned sequences. Its support values follow a similar scale and its receiver

Classification versus inference learning contrasted with real-world categories.

Science.gov (United States)

Jones, Erin L; Ross, Brian H

2011-07-01

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.

Statistical inference via fiducial methods

OpenAIRE

Salomé, Diemer

1998-01-01

In this thesis the attention is restricted to inductive reasoning using a mathematical probability model. A statistical procedure prescribes, for every theoretically possible set of data, the inference about the unknown of interest. ... Zie: Summary

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

Directory of Open Access Journals (Sweden)

Meyer Patrick

2007-01-01

Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

Directory of Open Access Journals (Sweden)

Patrick E. Meyer

2007-06-01

Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

Field occurrences and petrology of eclogites from the Dabie Mountains, Anhui, central China

Science.gov (United States)

Wang, X.; Jing, Y.; Liou, J. G.; Pan, G.; Liang, W.; Xia, M.; Maruyama, S.

1990-11-01

Four distinct types of eclogites are recognized according to their field occurrences and mineral parageneses in a gneiss terrane of the Dabie Mountains, a collision zone between the Sino-Korean and Yangtze cratons in central China. Some eclogites contain coesite and its quartz pseudomorphs enclosed in garnet and omphacite. Type I eclogites occur as layers in serpentinites and contain garnet, clinopyroxene, orthopyroxene, phengite, rutile, and coesite pseudomorph. Type II eclogites occur as lenticular bodies inside serpentinites and contain garnet, clinopyroxene, quartz, rutile, and edenitic hornblende. Type III eclogites occur as blocks of 2 cm to 20 m in size in a matrix of hornblende gneiss and biotite gneiss, and Type IV eclogites occur as thin layers interbedded with amphibolites. P- T estimates for these different eclogites indicate that they were formed under different physical conditions. All the eclogites were affected by later regional metamorphism for which the P- T conditions are estimated. This paper provides an introduction to the abundant eclogites from central China which have not been reported previously in Western literature. Specifically, the mode of field occurrence, petrography, mineral chemistry and formation conditions of the four types of eclogites are described. The paper is thus designed to establish a petrological framework for future detailed studies of the eclogites and their country rocks in an ancient zone of collision.

Subduction metamorphism in the Himalayan ultrahigh-pressure Tso Morari massif: An integrated geodynamic and petrological modelling approach

Science.gov (United States)

Palin, Richard M.; Reuber, Georg S.; White, Richard W.; Kaus, Boris J. P.; Weller, Owen M.

2017-06-01

The Tso Morari massif is one of only two regions where ultrahigh-pressure (UHP) metamorphism of subducted crust has been documented in the Himalayan Range. The tectonic evolution of the massif is enigmatic, as reported pressure estimates for peak metamorphism vary from ∼2.4 GPa to ∼4.8 GPa. This uncertainty is problematic for constructing large-scale numerical models of the early stages of India-Asia collision. To address this, we provide new constraints on the tectonothermal evolution of the massif via a combined geodynamic and petrological forward-modelling approach. A prograde-to-peak pressure-temperature-time (P-T-t) path has been derived from thermomechanical simulations tailored for Eocene subduction in the northwestern Himalaya. Phase equilibrium modelling performed along this P-T path has described the petrological evolution of felsic and mafic components of the massif crust, and shows that differences in their fluid contents would have controlled the degree of metamorphic phase transformation in each during subduction. Our model predicts that peak P-T conditions of ∼2.6-2.8 GPa and ∼600-620 ∘C, representative of 90-100 km depth (assuming lithostatic pressure), could have been reached just ∼3 Myr after the onset of subduction of continental crust. This P-T path and subduction duration correlate well with constraints reported for similar UHP eclogite in the Kaghan Valley, Pakistan Himalaya, suggesting that the northwest Himalaya contains dismembered remnants of what may have been a ∼400-km-long UHP terrane comparable in size to the Western Gneiss Region, Norway, and the Dabie-Sulu belt, China. A maximum overpressure of ∼0.5 GPa was calculated in our simulations for a homogeneous crust, although small-scale mechanical heterogeneities may produce overpressures that are larger in magnitude. Nonetheless, the extremely high pressures for peak metamorphism reported by some workers (up to 4.8 GPa) are unreliable owing to conventional thermobarometry

Petrologic and chemical changes in ductile shear zones as a function of depth in the continental crust

Science.gov (United States)

Yang, Xin-Yue

Petrologic and geochemical changes in ductile shear zones are important for understanding deformational and geochemical processes of the continental crust. This study examines three shear zones that formed under conditions varying from lower greenschist facies to upper amphibolite facies in order to document the petrologic and geochemical changes of deformed rocks at various metamorphic grades. The studied shear zones include two greenschist facies shear zones in the southern Appalachians and an upper amphibolite facies shear zone in southern Ontario. The mylonitic gneisses and mylonites in the Roses Mill shear zone of central Virginia are derived from a ferrodiorite protolith and characterized by a lower greenschist facies mineral assemblage. Both pressure solution and recrystallization were operative deformation mechanisms during mylonitization in this shear zone. Strain-driven dissolution and solution transfer played an important role in the mobilization of felsic components (Si, Al, K, Na, and Ca). During mylonitization, 17% to 32% bulk rock volume losses of mylonites are mainly attributed to removal of these mobile felsic components by a fluid phase. Mafic components (Fe, Mg, Ti, Mn and P) and trace elements, REE, Y, V and Sc, were immobile. At Rosman, North Carolina, the Brevard shear zone (BSZ) shows a deformational transition from the coarse-grained Henderson augen gneiss (HAG) to proto-mylonite, mylonite and ultra-mylonite. The mylonites contain a retrograde mineral assemblage as a product of fluid-assisted chemical breakdown of K-feldspar and biotite at higher greenschist facies conditions. Recrystallization and intra-crystalline plastic deformation are major deformation mechanisms in the BSZ. Fluid-assisted mylonitization in the BSZ led to 6% to 23% bulk volume losses in mylonites. During mylonitization, both major felsic and mafic elements and trace elements, Rb, Sr, Zr, V, Sc, and LREE were mobile; however, the HREEs were likely immobile. A shear zone

IMAGINE: Interstellar MAGnetic field INference Engine

Science.gov (United States)

Steininger, Theo

2018-03-01

IMAGINE (Interstellar MAGnetic field INference Engine) performs inference on generic parametric models of the Galaxy. The modular open source framework uses highly optimized tools and technology such as the MultiNest sampler (ascl:1109.006) and the information field theory framework NIFTy (ascl:1302.013) to create an instance of the Milky Way based on a set of parameters for physical observables, using Bayesian statistics to judge the mismatch between measured data and model prediction. The flexibility of the IMAGINE framework allows for simple refitting for newly available data sets and makes state-of-the-art Bayesian methods easily accessible particularly for random components of the Galactic magnetic field.

Geochemistry and petrology of selected coal samples from Sumatra, Kalimantan, Sulawesi, and Papua, Indonesia

International Nuclear Information System (INIS)

Belkin, Harvey E.; Tewalt, Susan J.; Hower, James C.; Stucker, J.D.; O'Keefe, J.M.K.

2009-01-01

Indonesia has become the world's largest exporter of thermal coal and is a major supplier to the Asian coal market, particularly as the People's Republic of China is now (2007) and perhaps may remain a net importer of coal. Indonesia has had a long history of coal production, mainly in Sumatra and Kalimantan, but only in the last two decades have government and commercial forces resulted in a remarkable coal boom. A recent assessment of Indonesian coal-bed methane (CBM) potential has motivated active CBM exploration. Most of the coal is Paleogene and Neogene, low to moderate rank and has low ash yield and sulfur (generally < 10 and < 1 wt.%, respectively). Active tectonic and igneous activity has resulted in significant rank increase in some coal basins. Eight coal samples are described that represent the major export and/or resource potential of Sumatra, Kalimantan, Sulawesi, and Papua. Detailed geochemistry, including proximate and ultimate analysis, sulfur forms, and major, minor, and trace element determinations are presented. Organic petrology and vitrinite reflectance data reflect various precursor flora assemblages and rank variations, including sample composites from active igneous and tectonic areas. A comparison of Hazardous Air Pollutants (HAPs) elements abundance with world and US averages show that the Indonesian coals have low combustion pollution potential. (author)

Inferring epidemic network topology from surveillance data.

Directory of Open Access Journals (Sweden)

Xiang Wan

Full Text Available The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases.

A Learning Algorithm for Multimodal Grammar Inference.

Science.gov (United States)

D'Ulizia, A; Ferri, F; Grifoni, P

2011-12-01

The high costs of development and maintenance of multimodal grammars in integrating and understanding input in multimodal interfaces lead to the investigation of novel algorithmic solutions in automating grammar generation and in updating processes. Many algorithms for context-free grammar inference have been developed in the natural language processing literature. An extension of these algorithms toward the inference of multimodal grammars is necessary for multimodal input processing. In this paper, we propose a novel grammar inference mechanism that allows us to learn a multimodal grammar from its positive samples of multimodal sentences. The algorithm first generates the multimodal grammar that is able to parse the positive samples of sentences and, afterward, makes use of two learning operators and the minimum description length metrics in improving the grammar description and in avoiding the over-generalization problem. The experimental results highlight the acceptable performances of the algorithm proposed in this paper since it has a very high probability of parsing valid sentences.

Bayesian Inference of High-Dimensional Dynamical Ocean Models

Science.gov (United States)

Lin, J.; Lermusiaux, P. F. J.; Lolla, S. V. T.; Gupta, A.; Haley, P. J., Jr.

2015-12-01

This presentation addresses a holistic set of challenges in high-dimension ocean Bayesian nonlinear estimation: i) predict the probability distribution functions (pdfs) of large nonlinear dynamical systems using stochastic partial differential equations (PDEs); ii) assimilate data using Bayes' law with these pdfs; iii) predict the future data that optimally reduce uncertainties; and (iv) rank the known and learn the new model formulations themselves. Overall, we allow the joint inference of the state, equations, geometry, boundary conditions and initial conditions of dynamical models. Examples are provided for time-dependent fluid and ocean flows, including cavity, double-gyre and Strait flows with jets and eddies. The Bayesian model inference, based on limited observations, is illustrated first by the estimation of obstacle shapes and positions in fluid flows. Next, the Bayesian inference of biogeochemical reaction equations and of their states and parameters is presented, illustrating how PDE-based machine learning can rigorously guide the selection and discovery of complex ecosystem models. Finally, the inference of multiscale bottom gravity current dynamics is illustrated, motivated in part by classic overflows and dense water formation sites and their relevance to climate monitoring and dynamics. This is joint work with our MSEAS group at MIT.

Hybrid Optical Inference Machines

Science.gov (United States)

1991-09-27

with labels. Now, events. a set of facts cal be generated in the dyadic form "u, R 1,2" Eichmann and Caulfield (19] consider the same type of and can...these enceding-schemes. These architectures are-based pri- 19. G. Eichmann and H. J. Caulfield, "Optical Learning (Inference)marily on optical inner

A Network Inference Workflow Applied to Virulence-Related Processes in Salmonella typhimurium

Energy Technology Data Exchange (ETDEWEB)

Taylor, Ronald C.; Singhal, Mudita; Weller, Jennifer B.; Khoshnevis, Saeed; Shi, Liang; McDermott, Jason E.

2009-04-20

Inference of the structure of mRNA transcriptional regulatory networks, protein regulatory or interaction networks, and protein activation/inactivation-based signal transduction networks are critical tasks in systems biology. In this article we discuss a workflow for the reconstruction of parts of the transcriptional regulatory network of the pathogenic bacterium Salmonella typhimurium based on the information contained in sets of microarray gene expression data now available for that organism, and describe our results obtained by following this workflow. The primary tool is one of the network inference algorithms deployed in the Software Environment for BIological Network Inference (SEBINI). Specifically, we selected the algorithm called Context Likelihood of Relatedness (CLR), which uses the mutual information contained in the gene expression data to infer regulatory connections. The associated analysis pipeline automatically stores the inferred edges from the CLR runs within SEBINI and, upon request, transfers the inferred edges into either Cytoscape or the plug-in Collective Analysis of Biological of Biological Interaction Networks (CABIN) tool for further post-analysis of the inferred regulatory edges. The following article presents the outcome of this workflow, as well as the protocols followed for microarray data collection, data cleansing, and network inference. Our analysis revealed several interesting interactions, functional groups, metabolic pathways, and regulons in S. typhimurium.

Training Inference Making Skills Using a Situation Model Approach Improves Reading Comprehension

Directory of Open Access Journals (Sweden)

Lisanne eBos

2016-02-01

Full Text Available This study aimed to enhance third and fourth graders’ text comprehension at the situation model level. Therefore, we tested a reading strategy training developed to target inference making skills, which are widely considered to be pivotal to situation model construction. The training was grounded in contemporary literature on situation model-based inference making and addressed the source (text-based versus knowledge-based, type (necessary versus unnecessary for (re-establishing coherence, and depth of an inference (making single lexical inferences versus combining multiple lexical inferences, as well as the type of searching strategy (forward versus backward. Results indicated that, compared to a control group (n = 51, children who followed the experimental training (n = 67 improved their inference making skills supportive to situation model construction. Importantly, our training also resulted in increased levels of general reading comprehension and motivation. In sum, this study showed that a ‘level of text representation’-approach can provide a useful framework to teach inference making skills to third and fourth graders.

Robust Demographic Inference from Genomic and SNP Data

Science.gov (United States)

Excoffier, Laurent; Dupanloup, Isabelle; Huerta-Sánchez, Emilia; Sousa, Vitor C.; Foll, Matthieu

2013-01-01

We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with , the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets. PMID:24204310

Universal Darwinism as a process of Bayesian inference

Directory of Open Access Journals (Sweden)

John Oberon Campbell

2016-06-01

Full Text Available Many of the mathematical frameworks describing natural selection are equivalent to Bayes’ Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians. As Bayesian inference can always be cast in terms of (variational free energy minimization, natural selection can be viewed as comprising two components: a generative model of an ‘experiment’ in the external world environment, and the results of that 'experiment' or the 'surprise' entailed by predicted and actual outcomes of the ‘experiment’. Minimization of free energy implies that the implicit measure of 'surprise' experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

Behavior Intention Derivation of Android Malware Using Ontology Inference

Directory of Open Access Journals (Sweden)

Jian Jiao

2018-01-01

Full Text Available Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system.

Genealogical and evolutionary inference with the human Y chromosome.

Science.gov (United States)

Stumpf, M P; Goldstein, D B

2001-03-02

Population genetics has emerged as a powerful tool for unraveling human history. In addition to the study of mitochondrial and autosomal DNA, attention has recently focused on Y-chromosome variation. Ambiguities and inaccuracies in data analysis, however, pose an important obstacle to further development of the field. Here we review the methods available for genealogical inference using Y-chromosome data. Approaches can be divided into those that do and those that do not use an explicit population model in genealogical inference. We describe the strengths and weaknesses of these model-based and model-free approaches, as well as difficulties associated with the mutation process that affect both methods. In the case of genealogical inference using microsatellite loci, we use coalescent simulations to show that relatively simple generalizations of the mutation process can greatly increase the accuracy of genealogical inference. Because model-free and model-based approaches have different biases and limitations, we conclude that there is considerable benefit in the continued use of both types of approaches.

SDG multiple fault diagnosis by real-time inverse inference

International Nuclear Information System (INIS)

Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng

2005-01-01

In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency

SDG multiple fault diagnosis by real-time inverse inference

Energy Technology Data Exchange (ETDEWEB)

Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng

2005-02-01

In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency.

Functional networks inference from rule-based machine learning models.

Science.gov (United States)

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

Petrology And Geochemistry Of Barite Mineralisation Around Azara North Central Nigeria

Directory of Open Access Journals (Sweden)

Tanko

2015-05-01

Full Text Available ABSTRACT The Azara barite deposits formed parts of Middle Benue Trough which is located in an elongated rift or faulted-bounded mega structural depression trending NE-SW to a length of over 1000 km and a width of 100 km.Petrological and geochemical investigations of Azrara barite deposits were carried out. Eight 8 selected samples of barites were collected from the veins four from known veins V1V3V17 and V 18 and four from new veins VAVBVCand VD werecarried out with the aim of determining their mineralisation potentials using petrographic studies and gravimetric method of analyses. The Petrographic studies of some of the thin section of the samples conducted using a polarizing microscope to determine the contents distributions and textures of the various veins Table 1. The weight percentage composition of barite in the samples are V1 86.39 VC82.61 V1881.48 V3 81.17 V17 79.82 VA78.94 VB76.82 and VD 70.55 respectively. It is deduced from this work that the chemical weathering of the carbonates resulted in two distinct types of barites Barite associated with mainly quartz SiO2 and limonite FeOOH.nH2O as major gangue and barite with siderite Ferrous Carbonate with high amount of Mg ankerite Ca Fe Mg CO3 and Calcite CaCO3. The outcomes were compared with the barite specification of Weigal1937 of 95.00 and were found to be good for making drilling mud for use in the oil industry paints and other chemicals

Causal inference based on counterfactuals

Directory of Open Access Journals (Sweden)

Höfler M

2005-09-01

Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.

Implementing and analyzing the multi-threaded LP-inference

Science.gov (United States)

Bolotova, S. Yu; Trofimenko, E. V.; Leschinskaya, M. V.

2018-03-01

The logical production equations provide new possibilities for the backward inference optimization in intelligent production-type systems. The strategy of a relevant backward inference is aimed at minimization of a number of queries to external information source (either to a database or an interactive user). The idea of the method is based on the computing of initial preimages set and searching for the true preimage. The execution of each stage can be organized independently and in parallel and the actual work at a given stage can also be distributed between parallel computers. This paper is devoted to the parallel algorithms of the relevant inference based on the advanced scheme of the parallel computations “pipeline” which allows to increase the degree of parallelism. The author also provides some details of the LP-structures implementation.

International Conference on Trends and Perspectives in Linear Statistical Inference

CERN Document Server

Rosen, Dietrich

2018-01-01

This volume features selected contributions on a variety of topics related to linear statistical inference. The peer-reviewed papers from the International Conference on Trends and Perspectives in Linear Statistical Inference (LinStat 2016) held in Istanbul, Turkey, 22-25 August 2016, cover topics in both theoretical and applied statistics, such as linear models, high-dimensional statistics, computational statistics, the design of experiments, and multivariate analysis. The book is intended for statisticians, Ph.D. students, and professionals who are interested in statistical inference. .

Packaging design as communicator of product attributes: Effects on consumers’ attribute inferences

NARCIS (Netherlands)

van Ooijen, I.

2016-01-01

This dissertation will focus on two types of attribute inferences that result from packaging design cues. First, the effects of product packaging design on quality related inferences are investigated. Second, the effects of product packaging design on healthiness related inferences are examined (See

Surrogate based approaches to parameter inference in ocean models

KAUST Repository

Knio, Omar

2016-01-06

This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.

Fast and scalable inference of multi-sample cancer lineages.

KAUST Repository

Popic, Victoria; Salari, Raheleh; Hajirasouliha, Iman; Kashef-Haghighi, Dorna; West, Robert B; Batzoglou, Serafim

2015-01-01

Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .

Fast and scalable inference of multi-sample cancer lineages.

KAUST Repository

Popic, Victoria

2015-05-06

Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .

Surrogate based approaches to parameter inference in ocean models

KAUST Repository

Knio, Omar

2016-01-01

This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.

Inferring causality from noisy time series data

DEFF Research Database (Denmark)

Mønster, Dan; Fusaroli, Riccardo; Tylén, Kristian

2016-01-01

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength...... and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise...

Preliminary results on a 4 kJ, 140 k A plasma focus

International Nuclear Information System (INIS)

Silva, Patricio; Favre, Mario; Chuaqui, Hernan; Wyndham, Edmund

1996-01-01

Preliminary results on the operation of a 4 kJ, 140 kA Plasma Focus device are presented. The machine operates Hydrogen and Hydrogen-Argon mixtures at pressures between 400 m Torr to 10 Torr. Main diagnostics include electric and current measurements, time and space resolved X-ray observations, with limited spectral resolution, and B-dot sensors to monitor the evolution of de current sheet during the run up phase of the discharge. The results indicate that good focus is obtained in the above pressure range. This is inferred from I-dot traces and Pin diode and pin-hole camera X-ray observations. The B-dot loops suggest that a symmetric current sheet is produced. These results show that the machine exhibits a reliable performance, which allows further studies on dense transient plasmas to be developed. (author)

Making Inferences in Adulthood: Falling Leaves Mean It's Fall.

Science.gov (United States)

Zandi, Taher; Gregory, Monica E.

1988-01-01

Assessed age differences in making inferences from prose. Older adults correctly answered mean of 10 questions related to implicit information and 8 related to explicit information. Young adults answered mean of 7 implicit and 12 explicit information questions. In spite of poorer recall of factual details, older subjects made inferences to greater…

Mixed normal inference on multicointegration

NARCIS (Netherlands)

Boswijk, H.P.

2009-01-01

Asymptotic likelihood analysis of cointegration in I(2) models, see Johansen (1997, 2006), Boswijk (2000) and Paruolo (2000), has shown that inference on most parameters is mixed normal, implying hypothesis test statistics with an asymptotic 2 null distribution. The asymptotic distribution of the

Baselines and test data for cross-lingual inference

DEFF Research Database (Denmark)

Agic, Zeljko; Schluter, Natalie

2018-01-01

The recent years have seen a revival of interest in textual entailment, sparked by i) the emergence of powerful deep neural network learners for natural language processing and ii) the timely development of large-scale evaluation datasets such as SNLI. Recast as natural language inference......, the problem now amounts to detecting the relation between pairs of statements: they either contradict or entail one another, or they are mutually neutral. Current research in natural language inference is effectively exclusive to English. In this paper, we propose to advance the research in SNLI-style natural...... language inference toward multilingual evaluation. To that end, we provide test data for four major languages: Arabic, French, Spanish, and Russian. We experiment with a set of baselines. Our systems are based on cross-lingual word embeddings and machine translation. While our best system scores an average...

Bayesian inference with ecological applications

CERN Document Server

Link, William A

2009-01-01

This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...

Nonparametric Bayesian inference in biostatistics

CERN Document Server

Müller, Peter

2015-01-01

As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...

Intracranial EEG correlates of implicit relational inference within the hippocampus.

Science.gov (United States)

Reber, T P; Do Lam, A T A; Axmacher, N; Elger, C E; Helmstaedter, C; Henke, K; Fell, J

2016-01-01

Drawing inferences from past experiences enables adaptive behavior in future situations. Inference has been shown to depend on hippocampal processes. Usually, inference is considered a deliberate and effortful mental act which happens during retrieval, and requires the focus of our awareness. Recent fMRI studies hint at the possibility that some forms of hippocampus-dependent inference can also occur during encoding and possibly also outside of awareness. Here, we sought to further explore the feasibility of hippocampal implicit inference, and specifically address the temporal evolution of implicit inference using intracranial EEG. Presurgical epilepsy patients with hippocampal depth electrodes viewed a sequence of word pairs, and judged the semantic fit between two words in each pair. Some of the word pairs entailed a common word (e.g., "winter-red," "red-cat") such that an indirect relation was established in following word pairs (e.g., "winter-cat"). The behavioral results suggested that drawing inference implicitly from past experience is feasible because indirect relations seemed to foster "fit" judgments while the absence of indirect relations fostered "do not fit" judgments, even though the participants were unaware of the indirect relations. A event-related potential (ERP) difference emerging 400 ms post-stimulus was evident in the hippocampus during encoding, suggesting that indirect relations were already established automatically during encoding of the overlapping word pairs. Further ERP differences emerged later post-stimulus (1,500 ms), were modulated by the participants' responses and were evident during encoding and test. Furthermore, response-locked ERP effects were evident at test. These ERP effects could hence be a correlate of the interaction of implicit memory with decision-making. Together, the data map out a time-course in which the hippocampus automatically integrates memories from discrete but related episodes to implicitly influence future

Estimating mountain basin-mean precipitation from streamflow using Bayesian inference

Science.gov (United States)

Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Lundquist, Jessica D.

2015-10-01

Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multimodel framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevation stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEA-inferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation.

Feature inference with uncertain categorization: Re-assessing Anderson's rational model.

Science.gov (United States)

Konovalova, Elizaveta; Le Mens, Gaël

2017-09-18

A key function of categories is to help predictions about unobserved features of objects. At the same time, humans are often in situations where the categories of the objects they perceive are uncertain. In an influential paper, Anderson (Psychological Review, 98(3), 409-429, 1991) proposed a rational model for feature inferences with uncertain categorization. A crucial feature of this model is the conditional independence assumption-it assumes that the within category feature correlation is zero. In prior research, this model has been found to provide a poor fit to participants' inferences. This evidence is restricted to task environments inconsistent with the conditional independence assumption. Currently available evidence thus provides little information about how this model would fit participants' inferences in a setting with conditional independence. In four experiments based on a novel paradigm and one experiment based on an existing paradigm, we assess the performance of Anderson's model under conditional independence. We find that this model predicts participants' inferences better than competing models. One model assumes that inferences are based on just the most likely category. The second model is insensitive to categories but sensitive to overall feature correlation. The performance of Anderson's model is evidence that inferences were influenced not only by the more likely category but also by the other candidate category. Our findings suggest that a version of Anderson's model which relaxes the conditional independence assumption will likely perform well in environments characterized by within-category feature correlation.

Integrating distributed Bayesian inference and reinforcement learning for sensor management

NARCIS (Netherlands)

Grappiolo, C.; Whiteson, S.; Pavlin, G.; Bakker, B.

2009-01-01

This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically

Reliability of dose volume constraint inference from clinical data

Science.gov (United States)

Lutz, C. M.; Møller, D. S.; Hoffmann, L.; Knap, M. M.; Alber, M.

2017-04-01

Dose volume histogram points (DVHPs) frequently serve as dose constraints in radiotherapy treatment planning. An experiment was designed to investigate the reliability of DVHP inference from clinical data for multiple cohort sizes and complication incidence rates. The experimental background was radiation pneumonitis in non-small cell lung cancer and the DVHP inference method was based on logistic regression. From 102 NSCLC real-life dose distributions and a postulated DVHP model, an ‘ideal’ cohort was generated where the most predictive model was equal to the postulated model. A bootstrap and a Cohort Replication Monte Carlo (CoRepMC) approach were applied to create 1000 equally sized populations each. The cohorts were then analyzed to establish inference frequency distributions. This was applied to nine scenarios for cohort sizes of 102 (1), 500 (2) to 2000 (3) patients (by sampling with replacement) and three postulated DVHP models. The Bootstrap was repeated for a ‘non-ideal’ cohort, where the most predictive model did not coincide with the postulated model. The Bootstrap produced chaotic results for all models of cohort size 1 for both the ideal and non-ideal cohorts. For cohort size 2 and 3, the distributions for all populations were more concentrated around the postulated DVHP. For the CoRepMC, the inference frequency increased with cohort size and incidence rate. Correct inference rates  >85 % were only achieved by cohorts with more than 500 patients. Both Bootstrap and CoRepMC indicate that inference of the correct or approximate DVHP for typical cohort sizes is highly uncertain. CoRepMC results were less spurious than Bootstrap results, demonstrating the large influence that randomness in dose-response has on the statistical analysis.

Inference in {open_quotes}poor{close_quotes} languages

Energy Technology Data Exchange (ETDEWEB)

Petrov, S. [Oak Ridge National Lab., TN (United States)

1996-12-31

Languages with a solvable implication problem but without complete and consistent systems of inference rules ({open_quote}poor{close_quote} languages) are considered. The problem of existence of a finite, complete, and consistent inference rule system for a {open_quotes}poor{close_quotes} language is stated independently of the language or the rule syntax. Several properties of the problem are proved. An application of the results to the language of join dependencies is given.

Inference of beliefs and emotions in patients with Alzheimer's disease.

Science.gov (United States)

Zaitchik, Deborah; Koff, Elissa; Brownell, Hiram; Winner, Ellen; Albert, Marilyn

2006-01-01

The present study compared 20 patients with mild to moderate Alzheimer's disease with 20 older controls (ages 69-94 years) on their ability to make inferences about emotions and beliefs in others. Six tasks tested their ability to make 1st-order and 2nd-order inferences as well as to offer explanations and moral evaluations of human action by appeal to emotions and beliefs. Results showed that the ability to infer emotions and beliefs in 1st-order tasks remains largely intact in patients with mild to moderate Alzheimer's. Patients were able to use mental states in the prediction, explanation, and moral evaluation of behavior. Impairment on 2nd-order tasks involving inference of mental states was equivalent to impairment on control tasks, suggesting that patients' difficulty is secondary to their cognitive impairments. ((c) 2006 APA, all rights reserved).

Inference method using bayesian network for diagnosis of pulmonary nodules

International Nuclear Information System (INIS)

Kawagishi, Masami; Iizuka, Yoshio; Yamamoto, Hiroyuki; Yakami, Masahiro; Kubo, Takeshi; Fujimoto, Koji; Togashi, Kaori

2010-01-01

This report describes the improvements of a naive Bayes model that infers the diagnosis of pulmonary nodules in chest CT images based on the findings obtained when a radiologist interprets the CT images. We have previously introduced an inference model using a naive Bayes classifier and have reported its clinical value based on evaluation using clinical data. In the present report, we introduce the following improvements to the original inference model: the selection of findings based on correlations and the generation of a model using only these findings, and the introduction of classifiers that integrate several simple classifiers each of which is specialized for specific diagnosis. These improvements were found to increase the inference accuracy by 10.4% (p<.01) as compared to the original model in 100 cases (222 nodules) based on leave-one-out evaluation. (author)

Generative inference for cultural evolution.

Science.gov (United States)

Kandler, Anne; Powell, Adam

2018-04-05

One of the major challenges in cultural evolution is to understand why and how various forms of social learning are used in human populations, both now and in the past. To date, much of the theoretical work on social learning has been done in isolation of data, and consequently many insights focus on revealing the learning processes or the distributions of cultural variants that are expected to have evolved in human populations. In population genetics, recent methodological advances have allowed a greater understanding of the explicit demographic and/or selection mechanisms that underlie observed allele frequency distributions across the globe, and their change through time. In particular, generative frameworks-often using coalescent-based simulation coupled with approximate Bayesian computation (ABC)-have provided robust inferences on the human past, with no reliance on a priori assumptions of equilibrium. Here, we demonstrate the applicability and utility of generative inference approaches to the field of cultural evolution. The framework advocated here uses observed population-level frequency data directly to establish the likely presence or absence of particular hypothesized learning strategies. In this context, we discuss the problem of equifinality and argue that, in the light of sparse cultural data and the multiplicity of possible social learning processes, the exclusion of those processes inconsistent with the observed data might be the most instructive outcome. Finally, we summarize the findings of generative inference approaches applied to a number of case studies.This article is part of the theme issue 'Bridging cultural gaps: interdisciplinary studies in human cultural evolution'. © 2018 The Author(s).

Inferring Domain Plans in Question-Answering

National Research Council Canada - National Science Library

Pollack, Martha E

1986-01-01

The importance of plan inference in models of conversation has been widely noted in the computational-linguistics literature, and its incorporation in question-answering systems has enabled a range...

Bayesian inference for hybrid discrete-continuous stochastic kinetic models

International Nuclear Information System (INIS)

Sherlock, Chris; Golightly, Andrew; Gillespie, Colin S

2014-01-01

We consider the problem of efficiently performing simulation and inference for stochastic kinetic models. Whilst it is possible to work directly with the resulting Markov jump process (MJP), computational cost can be prohibitive for networks of realistic size and complexity. In this paper, we consider an inference scheme based on a novel hybrid simulator that classifies reactions as either ‘fast’ or ‘slow’ with fast reactions evolving as a continuous Markov process whilst the remaining slow reaction occurrences are modelled through a MJP with time-dependent hazards. A linear noise approximation (LNA) of fast reaction dynamics is employed and slow reaction events are captured by exploiting the ability to solve the stochastic differential equation driving the LNA. This simulation procedure is used as a proposal mechanism inside a particle MCMC scheme, thus allowing Bayesian inference for the model parameters. We apply the scheme to a simple application and compare the output with an existing hybrid approach and also a scheme for performing inference for the underlying discrete stochastic model. (paper)

Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

DEFF Research Database (Denmark)

Brouwer, Thomas; Frellsen, Jes; Liò, Pietro

2017-01-01

In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri......-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real...

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

Science.gov (United States)

Fan, Jianqing; Tong, Xin; Zeng, Yao

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning , to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

Role of Utility and Inference in the Evolution of Functional Information

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Sharov, Alexei A.

2009-01-01

Functional information means an encoded network of functions in living organisms from molecular signaling pathways to an organism’s behavior. It is represented by two components: code and an interpretation system, which together form a self-sustaining semantic closure. Semantic closure allows some freedom between components because small variations of the code are still interpretable. The interpretation system consists of inference rules that control the correspondence between the code and the function (phenotype) and determines the shape of the fitness landscape. The utility factor operates at multiple time scales: short-term selection drives evolution towards higher survival and reproduction rate within a given fitness landscape, and long-term selection favors those fitness landscapes that support adaptability and lead to evolutionary expansion of certain lineages. Inference rules make short-term selection possible by shaping the fitness landscape and defining possible directions of evolution, but they are under control of the long-term selection of lineages. Communication normally occurs within a set of agents with compatible interpretation systems, which I call communication system. Functional information cannot be directly transferred between communication systems with incompatible inference rules. Each biological species is a genetic communication system that carries unique functional information together with inference rules that determine evolutionary directions and constraints. This view of the relation between utility and inference can resolve the conflict between realism/positivism and pragmatism. Realism overemphasizes the role of inference in evolution of human knowledge because it assumes that logic is embedded in reality. Pragmatism substitutes usefulness for truth and therefore ignores the advantage of inference. The proposed concept of evolutionary pragmatism rejects the idea that logic is embedded in reality; instead, inference rules are

Inference for shared-frailty survival models with left-truncated data

NARCIS (Netherlands)

van den Berg, G.J.; Drepper, B.

2016-01-01

Shared-frailty survival models specify that systematic unobserved determinants of duration outcomes are identical within groups of individuals. We consider random-effects likelihood-based statistical inference if the duration data are subject to left-truncation. Such inference with left-truncated

Parametric inference for biological sequence analysis.

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Pachter, Lior; Sturmfels, Bernd

2004-11-16

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

Improved Inference of Heteroscedastic Fixed Effects Models

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Afshan Saeed

2016-12-01

Full Text Available Heteroscedasticity is a stern problem that distorts estimation and testing of panel data model (PDM. Arellano (1987 proposed the White (1980 estimator for PDM with heteroscedastic errors but it provides erroneous inference for the data sets including high leverage points. In this paper, our attempt is to improve heteroscedastic consistent covariance matrix estimator (HCCME for panel dataset with high leverage points. To draw robust inference for the PDM, our focus is to improve kernel bootstrap estimators, proposed by Racine and MacKinnon (2007. The Monte Carlo scheme is used for assertion of the results.

A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

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Santra, Tapesh, E-mail: tapesh.santra@ucd.ie [Systems Biology Ireland, University College Dublin, Dublin (Ireland)

2014-05-20

Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

Inference of neuronal network spike dynamics and topology from calcium imaging data

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Henry eLütcke

2013-12-01

Full Text Available Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP occurrence ('spike trains' from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties.

A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

International Nuclear Information System (INIS)

Santra, Tapesh

2014-01-01

Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

Cortical hierarchies perform Bayesian causal inference in multisensory perception.

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Tim Rohe

2015-02-01

Full Text Available To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI, and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation. At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion. Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.

Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models

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Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro

2017-01-01

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in…

Hierarchical Active Inference: A Theory of Motivated Control.

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Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl J

2018-04-01

Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

SPEEDY: An Eclipse-based IDE for invariant inference

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David R. Cok

2014-04-01

Full Text Available SPEEDY is an Eclipse-based IDE for exploring techniques that assist users in generating correct specifications, particularly including invariant inference algorithms and tools. It integrates with several back-end tools that propose invariants and will incorporate published algorithms for inferring object and loop invariants. Though the architecture is language-neutral, current SPEEDY targets C programs. Building and using SPEEDY has confirmed earlier experience demonstrating the importance of showing and editing specifications in the IDEs that developers customarily use, automating as much of the production and checking of specifications as possible, and showing counterexample information directly in the source code editing environment. As in previous work, automation of specification checking is provided by back-end SMT solvers. However, reducing the effort demanded of software developers using formal methods also requires a GUI design that guides users in writing, reviewing, and correcting specifications and automates specification inference.

Efficient Exact Inference With Loss Augmented Objective in Structured Learning.

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Bauer, Alexander; Nakajima, Shinichi; Muller, Klaus-Robert

2016-08-19

Structural support vector machine (SVM) is an elegant approach for building complex and accurate models with structured outputs. However, its applicability relies on the availability of efficient inference algorithms--the state-of-the-art training algorithms repeatedly perform inference to compute a subgradient or to find the most violating configuration. In this paper, we propose an exact inference algorithm for maximizing nondecomposable objectives due to special type of a high-order potential having a decomposable internal structure. As an important application, our method covers the loss augmented inference, which enables the slack and margin scaling formulations of structural SVM with a variety of dissimilarity measures, e.g., Hamming loss, precision and recall, Fβ-loss, intersection over union, and many other functions that can be efficiently computed from the contingency table. We demonstrate the advantages of our approach in natural language parsing and sequence segmentation applications.

A general Bayes weibull inference model for accelerated life testing

International Nuclear Information System (INIS)

Dorp, J. Rene van; Mazzuchi, Thomas A.

2005-01-01

This article presents the development of a general Bayes inference model for accelerated life testing. The failure times at a constant stress level are assumed to belong to a Weibull distribution, but the specification of strict adherence to a parametric time-transformation function is not required. Rather, prior information is used to indirectly define a multivariate prior distribution for the scale parameters at the various stress levels and the common shape parameter. Using the approach, Bayes point estimates as well as probability statements for use-stress (and accelerated) life parameters may be inferred from a host of testing scenarios. The inference procedure accommodates both the interval data sampling strategy and type I censored sampling strategy for the collection of ALT test data. The inference procedure uses the well-known MCMC (Markov Chain Monte Carlo) methods to derive posterior approximations. The approach is illustrated with an example

A linear programming model for protein inference problem in shotgun proteomics.

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Huang, Ting; He, Zengyou

2012-11-15

Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is an important issue in shotgun proteomics. The objective of protein inference is to find a subset of proteins that are truly present in the sample. Although many methods have been proposed for protein inference, several issues such as peptide degeneracy still remain unsolved. In this article, we present a linear programming model for protein inference. In this model, we use a transformation of the joint probability that each peptide/protein pair is present in the sample as the variable. Then, both the peptide probability and protein probability can be expressed as a formula in terms of the linear combination of these variables. Based on this simple fact, the protein inference problem is formulated as an optimization problem: minimize the number of proteins with non-zero probabilities under the constraint that the difference between the calculated peptide probability and the peptide probability generated from peptide identification algorithms should be less than some threshold. This model addresses the peptide degeneracy issue by forcing some joint probability variables involving degenerate peptides to be zero in a rigorous manner. The corresponding inference algorithm is named as ProteinLP. We test the performance of ProteinLP on six datasets. Experimental results show that our method is competitive with the state-of-the-art protein inference algorithms. The source code of our algorithm is available at: https://sourceforge.net/projects/prolp/. zyhe@dlut.edu.cn. Supplementary data are available at Bioinformatics Online.

A petrological view of early Earth geodynamics

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Herzberg, C.

2003-04-01

Xenoliths of low T Archean cratonic mantle consist mostly of harzburgite and lherzolite with geochemical depletions that are characterisitc of igneous residues. Many authors have identified the complementary magmas as komatiites. This model is re-examined in light of work presented in Herzberg & O'Hara (2002) and found to be problematic. Munro-type alumina-undepleted komatiites from Alexo, Pyke Hill, and other locations often contain olivine phenocrysts with maximum Mg# \\cong 94. Residues of fractional melting would consist of pure dunite having Mg# = 97-98, but these are not observed. Residues of equilibrium melting would also be pure dunite with Mg# = 94, but these are also not observed. Olivines with Mg# = 94 are found in rare harzburgites, indicating that residues of alumina-undepleted komatiite have either been overprinted by subsequent magmatism or they have been geodynamically eroded. Alumina-undepleted komatiites can be successfully modeled with a primary magma containing 30% MgO produced by 0.5 mass fractions of equilibrium melting of depleted peridotite. A hot plume interpretation is consistent with both the petrology and helium isotopic compositions of alumina-undepleted komatiites. But what about cratonic mantle? The FeO and MgO contents of residues of fertile mantle peridotite formed by both equilibrium and fractional melting can be predicted and applied to xenoliths of cratonic mantle in most cases. Application to xenoliths from the Kaapvaal and Slave cratons is not possible owing to a second stage of Opx enrichment, but results can be applied to most xenoliths from Siberia, Tanzania, Somerset Island, and east Greenland as they contain less than 45% SiO_2. These xenoliths are very similar to residues produced by fractional melting. Pressures of initial melting were mostly 3 to 5 GPa, but can be as high 7 GPa. Pressures of final melting were highly variable and can be as low as 1 GPa. Potential temperatures (T_P) were typically 1450 to 1600oC and

Statistical inference on residual life

CERN Document Server

Jeong, Jong-Hyeon

2014-01-01

This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function, especially for communications between patients and physicians regarding the efficacy of a new drug in the medical field. This book reviews existing statistical methods to infer the residual life distribution. The review and comparison includes existing inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept of the residual life is also extended to competing risks analysis. The targeted audience includes biostatisticians, graduate students, and PhD (bio)statisticians. Knowledge in survival analysis at an introductory graduate level is advisable prior to reading this book.

Bayesian inference on proportional elections.

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Gabriel Hideki Vatanabe Brunello

Full Text Available Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.

Probability biases as Bayesian inference

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Andre; C. R. Martins

2006-11-01

Full Text Available In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions. %XX In that sense, it should be no surprise that humans reason with % probability as it has been observed.

The 2006-2009 activity of the Ubinas volcano (Peru): Petrology of the 2006 eruptive products and insights into genesis of andesite magmas, magma recharge and plumbing system

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Rivera, Marco; Thouret, Jean-Claude; Samaniego, Pablo; Le Pennec, Jean-Luc

2014-01-01

Following a fumarolic episode that started six months earlier, the most recent eruptive activity of the Ubinas volcano (south Peru) began on 27 March 2006, intensified between April and October 2006 and slowly declined until December 2009. The chronology of the explosive episode and the extent and composition of the erupted material are documented with an emphasis on ballistic ejecta. A petrological study of the juvenile products allows us to infer the magmatic processes related to the 2006-2009 eruptions of the andesitic Ubinas volcano. The juvenile magma erupted during the 2006 activity shows a homogeneous bulk-rock andesitic composition (56.7-57.6 wt.% SiO2), which belongs to a medium- to high-K calc-alkaline series. The mineral assemblage of the ballistic blocks and tephra consists of plagioclase > two-pyroxenes > Fe-Ti oxide and rare olivine and amphibole set in a groundmass of the same minerals with a dacitic composition (66-67 wt.% SiO2). Thermo-barometric data, based on two-pyroxene and amphibole stability, records a magma temperature of 998 ± 14 °C and a pressure of 476 ± 36 MPa. Widespread mineralogical and textural features point to a disequilibrium process in the erupted andesite magma. These features include inversely zoned "sieve textures" in plagioclase, inversely zoned clinopyroxene, and olivine crystals with reaction and thin overgrowth rims. They indicate that the pre-eruptive magmatic processes were dominated by recharge of a hotter mafic magma into a shallow reservoir, where magma mingling occurred and triggered the eruption. Prior to 2006, a probable recharge of a mafic magma produced strong convection and partial homogenization in the reservoir, as well as a pressure increase and higher magma ascent rate after four years of fumarolic activity. Mafic magmas do not prevail in the Ubinas pre-historical lavas and tephras. However, mafic andesites have been erupted during historical times (e.g. AD 1667 and 2006-2009 vulcanian eruptions). Hence

Evaluation of stationary and non-stationary geostatistical models for inferring hydraulic conductivity values at Aespoe

International Nuclear Information System (INIS)

La Pointe, P.R.

1994-11-01

This report describes the comparison of stationary and non-stationary geostatistical models for the purpose of inferring block-scale hydraulic conductivity values from packer tests at Aespoe. The comparison between models is made through the evaluation of cross-validation statistics for three experimental designs. The first experiment consisted of a 'Delete-1' test previously used at Finnsjoen. The second test consisted of 'Delete-10%' and the third test was a 'Delete-50%' test. Preliminary data analysis showed that the 3 m and 30 m packer test data can be treated as a sample from a single population for the purposes of geostatistical analyses. Analysis of the 3 m data does not indicate that there are any systematic statistical changes with depth, rock type, fracture zone vs non-fracture zone or other mappable factor. Directional variograms are ambiguous to interpret due to the clustered nature of the data, but do not show any obvious anisotropy that should be accounted for in geostatistical analysis. Stationary analysis suggested that there exists a sizeable spatially uncorrelated component ('Nugget Effect') in the 3 m data, on the order of 60% of the observed variance for the various models fitted. Four different nested models were automatically fit to the data. Results for all models in terms of cross-validation statistics were very similar for the first set of validation tests. Non-stationary analysis established that both the order of drift and the order of the intrinsic random functions is low. This study also suggests that conventional cross-validation studies and automatic variogram fitting are not necessarily evaluating how well a model will infer block scale hydraulic conductivity values. 20 refs, 20 figs, 14 tabs

Geochemistry and petrology of selected coal samples from Sumatra, Kalimantan, Sulawesi, and Papua, Indonesia

Energy Technology Data Exchange (ETDEWEB)

Belkin, Harvey E.; Tewalt, Susan J. [U.S. Geological Survey, 956 National Center, Reston, VA 20192 (United States); Hower, James C. [University of Kentucky Center for Applied Energy Research, 2540 Research Park Drive, Lexington, KY 40511 (United States); Stucker, J.D. [University of Kentucky Center for Applied Energy Research, 2540 Research Park Drive, Lexington, KY 40511 (United States)]|[University of Kentucky Department of Earth and Environmental Sciences, Lexington, KY 40506 (United States); O' Keefe, J.M.K. [Morehead State University, Department of Physical Science, Morehead, KY 40351 (United States)

2009-01-31

Indonesia has become the world's largest exporter of thermal coal and is a major supplier to the Asian coal market, particularly as the People's Republic of China is now (2007) and perhaps may remain a net importer of coal. Indonesia has had a long history of coal production, mainly in Sumatra and Kalimantan, but only in the last two decades have government and commercial forces resulted in a remarkable coal boom. A recent assessment of Indonesian coal-bed methane (CBM) potential has motivated active CBM exploration. Most of the coal is Paleogene and Neogene, low to moderate rank and has low ash yield and sulfur (generally < 10 and < 1 wt.%, respectively). Active tectonic and igneous activity has resulted in significant rank increase in some coal basins. Eight coal samples are described that represent the major export and/or resource potential of Sumatra, Kalimantan, Sulawesi, and Papua. Detailed geochemistry, including proximate and ultimate analysis, sulfur forms, and major, minor, and trace element determinations are presented. Organic petrology and vitrinite reflectance data reflect various precursor flora assemblages and rank variations, including sample composites from active igneous and tectonic areas. A comparison of Hazardous Air Pollutants (HAPs) elements abundance with world and US averages show that the Indonesian coals have low combustion pollution potential. (author)

Phylogenetic Inference of HIV Transmission Clusters

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Vlad Novitsky

2017-10-01

Full Text Available Better understanding the structure and dynamics of HIV transmission networks is essential for designing the most efficient interventions to prevent new HIV transmissions, and ultimately for gaining control of the HIV epidemic. The inference of phylogenetic relationships and the interpretation of results rely on the definition of the HIV transmission cluster. The definition of the HIV cluster is complex and dependent on multiple factors, including the design of sampling, accuracy of sequencing, precision of sequence alignment, evolutionary models, the phylogenetic method of inference, and specified thresholds for cluster support. While the majority of studies focus on clusters, non-clustered cases could also be highly informative. A new dimension in the analysis of the global and local HIV epidemics is the concept of phylogenetically distinct HIV sub-epidemics. The identification of active HIV sub-epidemics reveals spreading viral lineages and may help in the design of targeted interventions.HIVclustering can also be affected by sampling density. Obtaining a proper sampling density may increase statistical power and reduce sampling bias, so sampling density should be taken into account in study design and in interpretation of phylogenetic results. Finally, recent advances in long-range genotyping may enable more accurate inference of HIV transmission networks. If performed in real time, it could both inform public-health strategies and be clinically relevant (e.g., drug-resistance testing.

Causal inference of asynchronous audiovisual speech

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John F Magnotti

2013-11-01

Full Text Available During speech perception, humans integrate auditory information from the voice with visual information from the face. This multisensory integration increases perceptual precision, but only if the two cues come from the same talker; this requirement has been largely ignored by current models of speech perception. We describe a generative model of multisensory speech perception that includes this critical step of determining the likelihood that the voice and face information have a common cause. A key feature of the model is that it is based on a principled analysis of how an observer should solve this causal inference problem using the asynchrony between two cues and the reliability of the cues. This allows the model to make predictions abut the behavior of subjects performing a synchrony judgment task, predictive power that does not exist in other approaches, such as post hoc fitting of Gaussian curves to behavioral data. We tested the model predictions against the performance of 37 subjects performing a synchrony judgment task viewing audiovisual speech under a variety of manipulations, including varying asynchronies, intelligibility, and visual cue reliability. The causal inference model outperformed the Gaussian model across two experiments, providing a better fit to the behavioral data with fewer parameters. Because the causal inference model is derived from a principled understanding of the task, model parameters are directly interpretable in terms of stimulus and subject properties.

Functional neuroanatomy of intuitive physical inference.

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Fischer, Jason; Mikhael, John G; Tenenbaum, Joshua B; Kanwisher, Nancy

2016-08-23

To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.

General Purpose Probabilistic Programming Platform with Effective Stochastic Inference

Science.gov (United States)

2018-04-01

REFERENCES 74 LIST OF ACRONYMS 80 ii List of Figures Figure 1. The problem of inferring curves from data while simultaneously choosing the...bottom path) as the inverse problem to computer graphics (top path). ........ 18 Figure 18. An illustration of generative probabilistic graphics for 3D...Building these systems involves simultaneously developing mathematical models, inference algorithms and optimized software implementations. Small changes

Causal inference in survival analysis using pseudo-observations.

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Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T

2017-07-30

Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs to address right-censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with 'once and for all' by means of so-called pseudo-observations when doing causal inference in survival analysis. The pseudo-observations can be used as a replacement of the outcomes without censoring when applying 'standard' causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non-myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3-year overall survival probability and the 3-year risk of chronic graft-versus-host disease. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Sensorimotor Network Crucial for Inferring Amusement from Smiles.

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Paracampo, Riccardo; Tidoni, Emmanuele; Borgomaneri, Sara; di Pellegrino, Giuseppe; Avenanti, Alessio

2017-11-01

Understanding whether another's smile reflects authentic amusement is a key challenge in social life, yet, the neural bases of this ability have been largely unexplored. Here, we combined transcranial magnetic stimulation (TMS) with a novel empathic accuracy (EA) task to test whether sensorimotor and mentalizing networks are critical for understanding another's amusement. Participants were presented with dynamic displays of smiles and explicitly requested to infer whether the smiling individual was feeling authentic amusement or not. TMS over sensorimotor regions representing the face (i.e., in the inferior frontal gyrus (IFG) and ventral primary somatosensory cortex (SI)), disrupted the ability to infer amusement authenticity from observed smiles. The same stimulation did not affect performance on a nonsocial task requiring participants to track the smiling expression but not to infer amusement. Neither TMS over prefrontal and temporo-parietal areas supporting mentalizing, nor peripheral control stimulations, affected performance on either task. Thus, motor and somatosensory circuits for controlling and sensing facial movements are causally essential for inferring amusement from another's smile. These findings highlight the functional relevance of IFG and SI to amusement understanding and suggest that EA abilities may be grounded in sensorimotor networks for moving and feeling the body. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Formation of cratonic lithosphere: An integrated thermal and petrological model

Science.gov (United States)

Herzberg, Claude; Rudnick, Roberta

2012-09-01

The formation of cratonic mantle peridotite of Archean age is examined within the time frame of Earth's thermal history, and how it was expressed by temporal variations in magma and residue petrology. Peridotite residues that occupy the lithospheric mantle are rare owing to the effects of melt-rock reaction, metasomatism, and refertilization. Where they are identified, they are very similar to the predicted harzburgite residues of primary magmas of the dominant basalts in greenstone belts, which formed in a non-arc setting (referred to here as "non-arc basalts"). The compositions of these basalts indicate high temperatures of formation that are well-described by the thermal history model of Korenaga. In this model, peridotite residues of extensive ambient mantle melting had the highest Mg-numbers, lowest FeO contents, and lowest densities at ~ 2.5-3.5 Ga. These results are in good agreement with Re-Os ages of kimberlite-hosted cratonic mantle xenoliths and enclosed sulfides, and provide support for the hypothesis of Jordan that low densities of cratonic mantle are a measure of their high preservation potential. Cratonization of the Earth reached its zenith at ~ 2.5-3.5 Ga when ambient mantle was hot and extensive melting produced oceanic crust 30-45 km thick. However, there is a mass imbalance exhibited by the craton-wide distribution of harzburgite residues and the paucity of their complementary magmas that had compositions like the non-arc basalts. We suggest that the problem of the missing basaltic oceanic crust can be resolved by its hydration, cooling and partial transformation to eclogite, which caused foundering of the entire lithosphere. Some of the oceanic crust partially melted during foundering to produce continental crust composed of tonalite-trondhjemite-granodiorite (TTG). The remaining lithosphere gravitationally separated into 1) residual eclogite that continued its descent, and 2) buoyant harzburgite diapirs that rose to underplate cratonic nuclei

On the Grand Challenges in Physical Petrology: the Multiphase Crossroads

Science.gov (United States)

Bergantz, G. W.

2014-12-01

Rapid progress in experimental, micro-analytical and textural analysis at the crystal scale has produced an unprecedented record of magmatic processes. However an obstacle to further progress is the lack of understanding of how mass, energy and momentum flux associated with crystal-rich, open-system events produces identifiable outcomes. Hence developing a physically-based understanding of magmatic systems linking micro-scale petrological observations with a physical template operating at the macro-scale presents a so-called "Grand Challenge." The essence of this challenge is that magmatic systems have characteristic length and feedback scales between those accessible by classical continuum and discrete methods. It has become increasingly obvious that the old-school continuum methods have limited resolution and power of explanation for multiphase (real) magma dynamics. This is, in part, because in crystal-rich systems the deformation is non-affine, and so the concept of constitutive behavior is less applicable and likely not even relevant, especially if one is interested in the emergent character of micro-scale processes. One expression of this is the cottage industry of proposing viscosity laws for magmas, which serves as "blunt force" de facto corrections for what is intrinsically multiphase behavior. Even in more fluid-rich systems many of these laws are not suitable for use in the very transport theories they aim to support. The alternative approach is the discrete method, where multiphase interactions are explicitly resolved. This is a daunting prospect given the numbers of crystals in magmas. But perhaps all crystals don't need to be modeled. I will demonstrate how discrete methods can recover critical state behavior, resolve crystal migration, the onset of visco-elastic behavior such as melt-present shear bands which sets the large-scale mixing volumes, some of the general morpho-dynamics that underlies purported rheological models, and transient controls on

Coupled petrological-geodynamical modeling of a compositionally heterogeneous mantle plume

Science.gov (United States)

Rummel, Lisa; Kaus, Boris J. P.; White, Richard W.; Mertz, Dieter F.; Yang, Jianfeng; Baumann, Tobias S.

2018-01-01

Self-consistent geodynamic modeling that includes melting is challenging as the chemistry of the source rocks continuously changes as a result of melt extraction. Here, we describe a new method to study the interaction between physical and chemical processes in an uprising heterogeneous mantle plume by combining a geodynamic code with a thermodynamic modeling approach for magma generation and evolution. We pre-computed hundreds of phase diagrams, each of them for a different chemical system. After melt is extracted, the phase diagram with the closest bulk rock chemistry to the depleted source rock is updated locally. The petrological evolution of rocks is tracked via evolving chemical compositions of source rocks and extracted melts using twelve oxide compositional parameters. As a result, a wide variety of newly generated magmatic rocks can in principle be produced from mantle rocks with different degrees of depletion. The results show that a variable geothermal gradient, the amount of extracted melt and plume excess temperature affect the magma production and chemistry by influencing decompression melting and the depletion of rocks. Decompression melting is facilitated by a shallower lithosphere-asthenosphere boundary and an increase in the amount of extracted magma is induced by a lower critical melt fraction for melt extraction and/or higher plume temperatures. Increasing critical melt fractions activates the extraction of melts triggered by decompression at a later stage and slows down the depletion process from the metasomatized mantle. Melt compositional trends are used to determine melting related processes by focusing on K2O/Na2O ratio as indicator for the rock type that has been molten. Thus, a step-like-profile in K2O/Na2O might be explained by a transition between melting metasomatized and pyrolitic mantle components reproducible through numerical modeling of a heterogeneous asthenospheric mantle source. A potential application of the developed method

[Proceedings of the VII international symposium 'Cultural heritage in geosciences, mining and metallurgy : libraries, archives, museums' : "Museums and their collections" held at the Nationaal Natuurhistorisch Museum Leiden (The Netherlands), 19-23 May, 2003 / Cor F. Winkler Prins and Stephen K. Donovan (editors)]: Towards modern petrological collections

NARCIS (Netherlands)

Kriegsman, L.M.

2004-01-01

Petrological collections result from sampling for academic research, for aesthetic or commercial reasons, and to document natural diversity. Selection criteria for reducing and enhancing collections include adequate documentation, potential for future use, information density, time and money

Nonparametric statistical inference

CERN Document Server

Gibbons, Jean Dickinson

2010-01-01

Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente

Inference of gene-phenotype associations via protein-protein interaction and orthology.

Directory of Open Access Journals (Sweden)

Panwen Wang

Full Text Available One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.

Utilitarian Moral Judgment Exclusively Coheres with Inference from Is to Ought.

Science.gov (United States)

Elqayam, Shira; Wilkinson, Meredith R; Thompson, Valerie A; Over, David E; Evans, Jonathan St B T

2017-01-01

Faced with moral choice, people either judge according to pre-existing obligations ( deontological judgment), or by taking into account the consequences of their actions ( utilitarian judgment). We propose that the latter coheres with a more general cognitive mechanism - deontic introduction , the tendency to infer normative ('deontic') conclusions from descriptive premises (is-ought inference). Participants were presented with vignettes that allowed either deontological or utilitarian choice, and asked to draw a range of deontic conclusions, as well as judge the overall moral rightness of each choice separately. We predicted and found a selective defeasibility pattern, in which manipulations that suppressed deontic introduction also suppressed utilitarian moral judgment, but had little effect on deontological moral judgment. Thus, deontic introduction coheres with utilitarian moral judgment almost exclusively. We suggest a family of norm-generating informal inferences, in which normative conclusions are drawn from descriptive (although value-laden) premises. This family includes deontic introduction and utilitarian moral judgment as well as other informal inferences. We conclude with a call for greater integration of research in moral judgment and research into deontic reasoning and informal inference.

A Comparative Analysis of Fuzzy Inference Engines in Context of ...

African Journals Online (AJOL)

Fuzzy inference engine has found successful applications in a wide variety of fields, such as automatic control, data classification, decision analysis, expert engines, time series prediction, robotics, pattern recognition, etc. This paper presents a comparative analysis of three fuzzy inference engines, max-product, max-min ...

Believing What You're Told: Politeness and Scalar Inferences

Directory of Open Access Journals (Sweden)

Diana Mazzarella

2018-06-01

Full Text Available The experimental pragmatics literature has extensively investigated the ways in which distinct contextual factors affect the computation of scalar inferences, whose most studied example is the one that allows “Some X-ed” to mean Not all X-ed. Recent studies from Bonnefon et al. (2009, 2011 investigate the effect of politeness on the interpretation of scalar utterances. They argue that when the scalar utterance is face-threatening (“Some people hated your speech” (i the scalar inference is less likely to be derived, and (ii the semantic interpretation of “some” (at least some is arrived at slowly and effortfully. This paper re-evaluates the role of politeness in the computation of scalar inferences by drawing on the distinction between “comprehension” and “epistemic assessment” of communicated information. In two experiments, we test the hypothesis that, in these face-threatening contexts, scalar inferences are largely derived but are less likely to be accepted as true. In line with our predictions, we find that slowdowns in the face-threatening condition are attributable to longer reaction times at the (latter epistemic assessment stage, but not at the comprehension stage.

Inferring ontology graph structures using OWL reasoning

KAUST Repository

Rodriguez-Garcia, Miguel Angel

2018-01-05

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies\\' semantic content remains a challenge.We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies\\' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph .Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Inferring ontology graph structures using OWL reasoning.

Science.gov (United States)

Rodríguez-García, Miguel Ángel; Hoehndorf, Robert

2018-01-05

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Learning about the internal structure of categories through classification and feature inference.

Science.gov (United States)

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.

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Heiner Stuke

2017-01-01

Full Text Available Theoretical accounts suggest that an alteration in the brain's learning mechanisms might lead to overhasty inferences, resulting in psychotic symptoms. Here, we sought to elucidate the suggested link between maladaptive learning and psychosis. Ninety-eight healthy individuals with varying degrees of delusional ideation and hallucinatory experiences performed a probabilistic reasoning task that allowed us to quantify overhasty inferences. Replicating previous results, we found a relationship between psychotic experiences and overhasty inferences during probabilistic reasoning. Computational modelling revealed that the behavioral data was best explained by a novel computational learning model that formalizes the adaptiveness of learning by a non-linear distortion of prediction error processing, where an increased non-linearity implies a growing resilience against learning from surprising and thus unreliable information (large prediction errors. Most importantly, a decreased adaptiveness of learning predicted delusional ideation and hallucinatory experiences. Our current findings provide a formal description of the computational mechanisms underlying overhasty inferences, thereby empirically substantiating theories that link psychosis to maladaptive learning.

seXY: a tool for sex inference from genotype arrays.

Science.gov (United States)

Qian, David C; Busam, Jonathan A; Xiao, Xiangjun; O'Mara, Tracy A; Eeles, Rosalind A; Schumacher, Frederick R; Phelan, Catherine M; Amos, Christopher I

2017-02-15

Checking concordance between reported sex and genotype-inferred sex is a crucial quality control measure in genome-wide association studies (GWAS). However, limited insights exist regarding the true accuracy of software that infer sex from genotype array data. We present seXY, a logistic regression model trained on both X chromosome heterozygosity and Y chromosome missingness, that consistently demonstrated >99.5% sex inference accuracy in cross-validation for 889 males and 5,361 females enrolled in prostate cancer and ovarian cancer GWAS. Compared to PLINK, one of the most popular tools for sex inference in GWAS that assesses only X chromosome heterozygosity, seXY achieved marginally better male classification and 3% more accurate female classification. https://github.com/Christopher-Amos-Lab/seXY. Christopher.I.Amos@dartmouth.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Statistical inference from imperfect photon detection

International Nuclear Information System (INIS)

Audenaert, Koenraad M R; Scheel, Stefan

2009-01-01

We consider the statistical properties of photon detection with imperfect detectors that exhibit dark counts and less than unit efficiency, in the context of tomographic reconstruction. In this context, the detectors are used to implement certain positive operator-valued measures (POVMs) that would allow us to reconstruct the quantum state or quantum process under consideration. Here we look at the intermediate step of inferring outcome probabilities from measured outcome frequencies, and show how this inference can be performed in a statistically sound way in the presence of detector imperfections. Merging outcome probabilities for different sets of POVMs into a consistent quantum state picture has been treated elsewhere (Audenaert and Scheel 2009 New J. Phys. 11 023028). Single-photon pulsed measurements as well as continuous wave measurements are covered.

Role of Speaker Cues in Attention Inference

Directory of Open Access Journals (Sweden)

Jin Joo Lee

2017-10-01

Full Text Available Current state-of-the-art approaches to emotion recognition primarily focus on modeling the nonverbal expressions of the sole individual without reference to contextual elements such as the co-presence of the partner. In this paper, we demonstrate that the accurate inference of listeners’ social-emotional state of attention depends on accounting for the nonverbal behaviors of their storytelling partner, namely their speaker cues. To gain a deeper understanding of the role of speaker cues in attention inference, we conduct investigations into real-world interactions of children (5–6 years old storytelling with their peers. Through in-depth analysis of human–human interaction data, we first identify nonverbal speaker cues (i.e., backchannel-inviting cues and listener responses (i.e., backchannel feedback. We then demonstrate how speaker cues can modify the interpretation of attention-related backchannels as well as serve as a means to regulate the responsiveness of listeners. We discuss the design implications of our findings toward our primary goal of developing attention recognition models for storytelling robots, and we argue that social robots can proactively use speaker cues to form more accurate inferences about the attentive state of their human partners.

Likelihood inference for unions of interacting discs

DEFF Research Database (Denmark)

Møller, Jesper; Helisová, Katarina

To the best of our knowledge, this is the first paper which discusses likelihood inference or a random set using a germ-grain model, where the individual grains are unobservable edge effects occur, and other complications appear. We consider the case where the grains form a disc process modelled...... is specified with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analyzing Peter Diggle's heather dataset, where we discuss the results...... of simulation-based maximum likelihood inference and the effect of specifying different reference Poisson models....

Likelihood inference for unions of interacting discs

DEFF Research Database (Denmark)

Møller, Jesper; Helisova, K.

2010-01-01

This is probably the first paper which discusses likelihood inference for a random set using a germ-grain model, where the individual grains are unobservable, edge effects occur and other complications appear. We consider the case where the grains form a disc process modelled by a marked point...... process, where the germs are the centres and the marks are the associated radii of the discs. We propose to use a recent parametric class of interacting disc process models, where the minimal sufficient statistic depends on various geometric properties of the random set, and the density is specified......-based maximum likelihood inference and the effect of specifying different reference Poisson models....

Development of the Bayesian method for unavailability inference. The new inferential theory and the examples of inference using BWR outage data in Japan

International Nuclear Information System (INIS)

Nakamura, Makoto

2009-01-01

It is important for Level 1 PSA to quantify input reliability parameters and their uncertainty. Bayesian methods for inference of system/component unavailability, however, are not well studied. At present practitioners allocate the uncertainty (i.e. error factor) of the unavailability based on engineering judgment. Systematic methods based on Bayesian statistics are needed for quantification of such uncertainty. In this study we have developed a new method for Bayesian inference of unavailability, where the posterior of system/component unavailability is described by the inverted gamma distribution. We show that the average of the posterior comes close to the point estimate of the unavailability as the number of outages goes to infinity. That indicates validity of the new method. Using plant data recorded in NUCIA, we have applied the new method to inference of system unavailability under unplanned outages due to violations of LCO at BWRs in Japan. According to the inference results, the unavailability is populated in the order of 10 -5 -10 -4 and the error factor is within 1-2. Thus, the new Bayesian method allows one to quantify magnitudes and widths (i.e. error factor) of uncertainty distributions of unavailability. (author)

Simultaneous inference for model averaging of derived parameters

DEFF Research Database (Denmark)

Jensen, Signe Marie; Ritz, Christian

2015-01-01

Model averaging is a useful approach for capturing uncertainty due to model selection. Currently, this uncertainty is often quantified by means of approximations that do not easily extend to simultaneous inference. Moreover, in practice there is a need for both model averaging and simultaneous...... inference for derived parameters calculated in an after-fitting step. We propose a method for obtaining asymptotically correct standard errors for one or several model-averaged estimates of derived parameters and for obtaining simultaneous confidence intervals that asymptotically control the family...

Sedimentation on the Valencia Continental Shelf: Preliminary results

Science.gov (United States)

Maldonado, Andres; Swift, Donald J. P.; Young, Robert A.; Han, Gregory; Nittrouer, Charles A.; DeMaster, David J.; Rey, Jorge; Palomo, Carlos; Acosta, Juan; Ballester, A.; Castellvi, J.

1983-10-01

Preliminary analysis of data collected during the course of a cooperative Spanish-United States investigation of the Valencia Shelf (western Mediterranean) reveals a storm-dominated, mud-accumulating sedimentary regime. Calcareous mud is accumulating seaward of a narrow band of shoreface sand and gravel. On the outer shelf the mud is enriched by a pelagic calcareous component. Preliminary 210Pb data from vertical profiles of box cores yield nominal accumulation rates from 2.6 mm y -1 near the Ebro Delta to 1.3 mm y -1 on the southern portion of the Valencia Shelf. Storm-current winnowing has resulted in the development of a biogenic lag sand over the mid-shelf mud in the northern part of the study area. Piston cores reveal a basal Holocene sand and gravel facies similar to that presently seen on the inner shelf. Upward-fining sequences on the central and outer shelf are inferred to result from the landward shift of lithotopes during the course of the Holocene transgression. These sequences are locally repeated, perhaps as the consequence of brief, local interludes of coastal progradation. Application of a diagnostic circulation model suggests that intense, downwelling coastal flows occur during winter northeastern storms. Storm activity has induced erosional shoreface retreat during the course of the Holocene transgression and has generated by this means the basal coarse facies observed in the piston cores. In the central part of the study area seaward of the Albufera Lagoon, the mud blanket thins to a layer several centimeters thick which is draped over a thickened (10 m) basal sand. The basal sand is molded into northwest trending ridges. The data are not sufficient to determine whether these are overstepped barriers, or submarine sand ridges formed by storm flows during the shoreface retreat process.

Congested Link Inference Algorithms in Dynamic Routing IP Network

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Yu Chen

2017-01-01

Full Text Available The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP and Rest Bayesian Network Model (RBNM, we proposed an Improved CLINK (ICLINK algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.

Inference of population history and patterns from molecular data

DEFF Research Database (Denmark)

Tataru, Paula

, the existing mathematical models and computational methods need to be reformulated. I address this from an inference perspective in two areas of bioinformatics. Population genetics studies the influence exerted by various factors on the dynamics of a population's genetic variation. These factors cover...... evolutionary forces, such as mutation and selection, but also changes in population size. The aim in population genetics is to untangle the history of a population from observed genetic variation. This subject is dominated by two dual models, the Wright-Fisher and coalescent. I first introduce a new...... approximation to the Wright-Fisher model, which I show to accurately infer split times between populations. This approximation can potentially be applied for inference of mutation rates and selection coefficients. I then illustrate how the coalescent process is the natural framework for detecting traces...

Modeling and control of an unstable system using probabilistic fuzzy inference system

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Sozhamadevi N.

2015-09-01

Full Text Available A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

Illusory inferences from a disjunction of conditionals: a new mental models account.

Science.gov (United States)

Barrouillet, P; Lecas, J F

2000-08-14

(Johnson-Laird, P.N., & Savary, F. (1999, Illusory inferences: a novel class of erroneous deductions. Cognition, 71, 191-229.) have recently presented a mental models account, based on the so-called principle of truth, for the occurrence of inferences that are compelling but invalid. This article presents an alternative account of the illusory inferences resulting from a disjunction of conditionals. In accordance with our modified theory of mental models of the conditional, we show that the way individuals represent conditionals leads them to misinterpret the locus of the disjunction and prevents them from drawing conclusions from a false conditional, thus accounting for the compelling character of the illusory inference.

Statistical Inference and Patterns of Inequality in the Global North

Science.gov (United States)

Moran, Timothy Patrick

2006-01-01

Cross-national inequality trends have historically been a crucial field of inquiry across the social sciences, and new methodological techniques of statistical inference have recently improved the ability to analyze these trends over time. This paper applies Monte Carlo, bootstrap inference methods to the income surveys of the Luxembourg Income…

A Comparative Analysis of Fuzzy Inference Engines in Context of ...

African Journals Online (AJOL)

PROF. O. E. OSUAGWU

Fuzzy Inference engine is an important part of reasoning systems capable of extracting correct conclusions from ... is known as the inference, or rule definition portion, of fuzzy .... minimal set of decision rules based on input- ... The study uses Mamdani FIS model and. Sugeno FIS ... control of induction motor drive. [18] study.

Bayesian inference for partially identified models exploring the limits of limited data

CERN Document Server

Gustafson, Paul

2015-01-01

Introduction Identification What Is against Us? What Is for Us? Some Simple Examples of Partially Identified ModelsThe Road Ahead The Structure of Inference in Partially Identified Models Bayesian Inference The Structure of Posterior Distributions in PIMs Computational Strategies Strength of Bayesian Updating, Revisited Posterior MomentsCredible Intervals Evaluating the Worth of Inference Partial Identification versus Model Misspecification The Siren Call of Identification Comp

Scalable inference for stochastic block models

KAUST Repository

Peng, Chengbin; Zhang, Zhihua; Wong, Ka-Chun; Zhang, Xiangliang; Keyes, David E.

2017-01-01

Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of "big data," traditional inference

Deep electromagnetic sounding of the moon with Lunokhod 2 data

Science.gov (United States)

Vanyan, L. L.; Yegorov, I. V.; Faynberg, E. B.

1977-01-01

Results of electromagnetic sounding distinguished an outer high resistance shell about 200 km thick in the moon's structure. A preliminary petrological interpretation of the moon's layers indicated their origin as a consequence of differentiation of the initial peridotite material. Upon melting, 20% to 40% of the material melts and is removed to form a high resistance basaltic shell underlain by a layer of spinal peridotites enriched in divalent iron oxides and having a reduced resistance.

Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

Science.gov (United States)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

2016-05-01

Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert

Statistical inferences for bearings life using sudden death test

Directory of Open Access Journals (Sweden)

Morariu Cristin-Olimpiu

2017-01-01

Full Text Available In this paper we propose a calculus method for reliability indicators estimation and a complete statistical inferences for three parameters Weibull distribution of bearings life. Using experimental values regarding the durability of bearings tested on stands by the sudden death tests involves a series of particularities of the estimation using maximum likelihood method and statistical inference accomplishment. The paper detailing these features and also provides an example calculation.

Assessing school-aged children's inference-making: the effect of story test format in listening comprehension.

Science.gov (United States)

Freed, Jenny; Cain, Kate

2017-01-01

Comprehension is critical for classroom learning and educational success. Inferences are integral to good comprehension: successful comprehension requires the listener to generate local coherence inferences, which involve integrating information between clauses, and global coherence inferences, which involve integrating textual information with background knowledge to infer motivations, themes, etc. A central priority for the diagnosis of comprehension difficulties and our understanding of why these difficulties arise is the development of valid assessment instruments. We explored typically developing children's ability to make local and global coherence inferences using a novel assessment of listening comprehension. The aims were to determine whether children were more likely to make the target inferences when these were asked during story presentation versus after presentation of the story, and whether there were any age differences between conditions. Children in Years 3 (n = 29) and 5 (n = 31) listened to short stories presented either in a segmented format, in which questions to assess local and global coherence inferences were asked at specific points during story presentation, or in a whole format, when all the questions were asked after the story had been presented. There was developmental progression between age groups for both types of inference question. Children also scored higher on the global coherence inference questions than the local coherence inference questions. There was a benefit of the segmented format for younger children, particularly for the local inference questions. The results suggest that children are more likely to make target inferences if prompted during presentation of the story, and that this format is particularly facilitative for younger children and for local coherence inferences. This has implications for the design of comprehension assessments as well as for supporting children with comprehension difficulties in the classroom

Deontic Introduction: A Theory of Inference from Is to Ought

Science.gov (United States)

Elqayam, Shira; Thompson, Valerie A.; Wilkinson, Meredith R.; Evans, Jonathan St. B. T.; Over, David E.

2015-01-01

Humans have a unique ability to generate novel norms. Faced with the knowledge that there are hungry children in Somalia, we easily and naturally infer that we ought to donate to famine relief charities. Although a contentious and lively issue in metaethics, such inference from "is" to "ought" has not been systematically…

Image-Data Compression Using Edge-Optimizing Algorithm for WFA Inference.

Science.gov (United States)

Culik, Karel II; Kari, Jarkko

1994-01-01

Presents an inference algorithm that produces a weighted finite automata (WFA), in particular, the grayness functions of graytone images. Image-data compression results based on the new inference algorithm produces a WFA with a relatively small number of edges. Image-data compression results alone and in combination with wavelets are discussed.…

Causal learning and inference as a rational process: the new synthesis.

Science.gov (United States)

Holyoak, Keith J; Cheng, Patricia W

2011-01-01

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.

Utilitarian Moral Judgment Exclusively Coheres with Inference from Is to Ought

Directory of Open Access Journals (Sweden)

Shira Elqayam

2017-06-01

Full Text Available Faced with moral choice, people either judge according to pre-existing obligations (deontological judgment, or by taking into account the consequences of their actions (utilitarian judgment. We propose that the latter coheres with a more general cognitive mechanism – deontic introduction, the tendency to infer normative (‘deontic’ conclusions from descriptive premises (is-ought inference. Participants were presented with vignettes that allowed either deontological or utilitarian choice, and asked to draw a range of deontic conclusions, as well as judge the overall moral rightness of each choice separately. We predicted and found a selective defeasibility pattern, in which manipulations that suppressed deontic introduction also suppressed utilitarian moral judgment, but had little effect on deontological moral judgment. Thus, deontic introduction coheres with utilitarian moral judgment almost exclusively. We suggest a family of norm-generating informal inferences, in which normative conclusions are drawn from descriptive (although value-laden premises. This family includes deontic introduction and utilitarian moral judgment as well as other informal inferences. We conclude with a call for greater integration of research in moral judgment and research into deontic reasoning and informal inference.

New developments of a knowledge based system (VEG) for inferring vegetation characteristics

Science.gov (United States)

Kimes, D. S.; Harrison, P. A.; Harrison, P. R.

1992-01-01

An extraction technique for inferring physical and biological surface properties of vegetation using nadir and/or directional reflectance data as input has been developed. A knowledge-based system (VEG) accepts spectral data of an unknown target as input, determines the best strategy for inferring the desired vegetation characteristic, applies the strategy to the target data, and provides a rigorous estimate of the accuracy of the inference. Progress in developing the system is presented. VEG combines methods from remote sensing and artificial intelligence, and integrates input spectral measurements with diverse knowledge bases. VEG has been developed to (1) infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; (2) test and develop new extraction techniques on an internal spectral database; (3) browse, plot, or analyze directional reflectance data in the system's spectral database; (4) discriminate between user-defined vegetation classes using spectral and directional reflectance relationships; and (5) infer unknown view angles from known view angles (known as view angle extension).

Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.

Directory of Open Access Journals (Sweden)

Richard R Stein

2015-07-01

Full Text Available Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.

Generic patch inference

DEFF Research Database (Denmark)

Andersen, Jesper; Lawall, Julia

2010-01-01

A key issue in maintaining Linux device drivers is the need to keep them up to date with respect to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spdiff, that identifies common changes...... developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect...

Inferring time-varying network topologies from gene expression data.

Science.gov (United States)

Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

Network Model-Assisted Inference from Respondent-Driven Sampling Data.

Science.gov (United States)

Gile, Krista J; Handcock, Mark S

2015-06-01

Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population.

Models for probability and statistical inference theory and applications

CERN Document Server

Stapleton, James H

2007-01-01

This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readersModels for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses mo...

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

Science.gov (United States)

Orhan, A Emin; Ma, Wei Ji

2017-07-26

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

Cortical information flow during inferences of agency

Directory of Open Access Journals (Sweden)

Myrthel eDogge

2014-08-01

Full Text Available Building on the recent finding that agency experiences do not merely rely on sensorimotor information but also on cognitive cues, this exploratory study uses electroencephalographic recordings to examine functional connectivity during agency inference processing in a setting where action and outcome are independent. Participants completed a computerized task in which they pressed a button followed by one of two color words (red or blue and rated their experienced agency over producing the color. Before executing the action, a matching or mismatching color word was pre-activated by explicitly instructing participants to produce the color (goal condition or by briefly presenting the color word (prime condition. In both conditions, experienced agency was higher in matching versus mismatching trials. Furthermore, increased electroencephalography (EEG-based connectivity strength was observed between parietal and frontal nodes and within the (prefrontal cortex when color-outcomes matched with goals and participants reported high agency. This pattern of increased connectivity was not identified in trials where outcomes were pre-activated through primes. These results suggest that different connections are involved in the experience and in the loss of agency, as well as in inferences of agency resulting from different types of pre-activation. Moreover, the findings provide novel support for the involvement of a fronto-parietal network in agency inferences.

Preliminary results of a recent cruise to the Northern Central Indian Ridge

Digital Repository Service at National Institute of Oceanography (India)

Drolia, R; Iyer, S.D.; Chakraborty, B.; Kodagali, V.N.; Mukhopadhyay, R; Nanyasi, S.K.; Sarma, K.V.L.N.S.; Rajasekhar, R; Misra, S.; Ray, Dwijesh; Andrade, R; Lasitha, S.; Varghese, J.; Jacob, J.; Sukumaran, N.P.; Pednekar, A.; Furtado, R; Nair, A.

. This paper is published with the permission of the Directors of NIO and NGRI. The project is funded by the United States India Fund through ONR Grant # N 00014-97-I-0925. References Cande S.C. and Kent D.V. 1995, Revised calibration of the geomagnetic... al., 2000), results of reconnaissance survey during 28 th cruise of R/V Sonne (1983), no data of significance is available from the study areas. In the NCIR lacunae exist con- cerning plate kinematics, segmenta- tion pattern, petrologic variations...

Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology

Science.gov (United States)

Alsing, Justin; Wandelt, Benjamin; Feeney, Stephen

2018-03-01

Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference from these models using only forward simulations, free from any likelihood assumptions or approximations. Likelihood-free inference generically involves simulating mock data and comparing to the observed data; this comparison in data-space suffers from the curse of dimensionality and requires compression of the data to a small number of summary statistics to be tractable. In this paper we use massive asymptotically-optimal data compression to reduce the dimensionality of the data-space to just one number per parameter, providing a natural and optimal framework for summary statistic choice for likelihood-free inference. Secondly, we present the first cosmological application of Density Estimation Likelihood-Free Inference (DELFI), which learns a parameterized model for joint distribution of data and parameters, yielding both the parameter posterior and the model evidence. This approach is conceptually simple, requires less tuning than traditional Approximate Bayesian Computation approaches to likelihood-free inference and can give high-fidelity posteriors from orders of magnitude fewer forward simulations. As an additional bonus, it enables parameter inference and Bayesian model comparison simultaneously. We demonstrate Density Estimation Likelihood-Free Inference with massive data compression on an analysis of the joint light-curve analysis supernova data, as a simple validation case study. We show that high-fidelity posterior inference is possible for full-scale cosmological data analyses with as few as ˜104 simulations, with substantial scope for further improvement, demonstrating the scalability of likelihood-free inference to large and complex cosmological datasets.

Evaluation of artificial time series microarray data for dynamic gene regulatory network inference.

Science.gov (United States)

Xenitidis, P; Seimenis, I; Kakolyris, S; Adamopoulos, A

2017-08-07

High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process. We examined the effect of various factors on the inference performance such as the network size, the presence of noise in microarray data, and the network sparseness. We used a system theory approach and examined the relationship between the pole placement of the inferred system and the inference performance. We examined the relationship between the inference performance in the time domain and the true system parameter identification. Simulation results indicated that time separation and the percentage of directly triggered genes are crucial factors. Also, network sparseness, the triggering function type and noise in input data affect the inference performance. When two factors were simultaneously varied, it was found that variation of one parameter significantly affects the dynamic response of the other. Crucial factors were also examined using a real GRN and acquired results confirmed simulation findings with artificial data. Different initial conditions were also used as an alternative triggering approach. Relevant results confirmed that the number of datasets constitutes the most significant parameter with regard to the inference performance. Copyright © 2017 Elsevier

New Petrology, Mineral Chemistry and Stable MG Isotope Compositions of an Allende CAI: EK-459-7-2

Science.gov (United States)

Jeffcoat, C. R.; Kerekgyarto, A. G.; Lapen, T. J.; Righter, M.; Simon, J. I.; Ross, D. K.

2016-01-01

Calcium-aluminum-rich inclusions (CAIs) are the key to understanding physical and chemical conditions in the nascent solar nebula. These inclusions have the oldest radiometric ages of solar system materials and are composed of phases that are predicted to condense early from a gas of solar composition. Thus, their chemistry and textures record conditions and processes in the earliest stages of development of the solar nebula. Type B inclusions are typically larger and more coarse grained than other types with substantial evidence that many of them were at least partially molten. Type B inclusions are further subdivided into Type B1 (possess thick melilite mantle) and Type B2 (lack melilite mantle). Despite being extensively studied, the origin of the melilite mantles of Type B1 inclusions remains uncertain. We present petrologic and chemical data for a Type B inclusion, EK-459-7-2, that bears features found in both Type B1 and B2 inclusions and likely represents an intermediate between the two types. Detailed studies of more of these intermediate objects may help to constrain models for Type B1 rim formation.

Ontological Constraints in Children's Inductive Inferences: Evidence From a Comparison of Inferences Within Animals and Vehicles.

Science.gov (United States)

Tarlowski, Andrzej

2018-01-01

There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects.

Processing of Scalar Inferences by Mandarin Learners of English: An Online Measure.

Directory of Open Access Journals (Sweden)

Yowyu Lin

Full Text Available Scalar inferences represent the condition when a speaker uses a weaker expression such as some in a pragmatic scale like , and s/he has the intention to reject the stronger use of the other word like all in the utterance. Considerable disagreement has arisen concerning how interlocutors derive the inferences. The study presented here tries to address this issue by examining online scalar inferences among Mandarin learners of English. To date, Default Inference and Relevance Theory have made different predictions regarding how people process scalar inferences. Findings from recently emerging first language studies did not fully resolved the debate but led to even more heated debates. The current three online psycholinguistic experiments reported here tried to address the processing of scalar inferences from second language perspective. Results showed that Mandarin learners of English showed faster reaction times and a higher acceptance rate when interpreting some as some but not all and this was true even when subjects were under time pressure, which was manifested in Experiment 2. Overall, the results of the experiments supported Default Theory. In addition, Experiment 3 also found that working memory capacity plays a critical role during scalar inference processing. High span readers were faster in accepting the some but not all interpretation than low span readers. However, compared with low span readers, high span readers were more likely to accept the some and possibly all condition, possibly due to their working memory capacity to generate scenarios to fit the interpretation.

Probabilistic Decision Graphs - Combining Verification and AI Techniques for Probabilistic Inference

DEFF Research Database (Denmark)

Jaeger, Manfred

2004-01-01

We adopt probabilistic decision graphs developed in the field of automated verification as a tool for probabilistic model representation and inference. We show that probabilistic inference has linear time complexity in the size of the probabilistic decision graph, that the smallest probabilistic ...

Petrological-geochemical characteristics of coarse-grained clastic sedimentary rocks of Quantou Formation, Cretaceous in Songliao basin and their geological significance

International Nuclear Information System (INIS)

Wang Gan; Zhang Bangtong

2005-01-01

Clastic sedimentary rocks of Quantou Formation, Cretaceous in Qing-an area, Songliao basin are mainly composed of sandstone, mudstone and siltstone. The petrological-chemical analysis of clastic sedimentary rocks from Quantou Formation, Cretaceous indicates that their lithology mainly consists of arkose, shale and minor rock debris sandstone and greywacke by chemical classification of bulk elements. REE distribution pattern displays the apparent enrichment of LREE and negative anomaly of Eu and is similar to that of NASC and PAAS. The ratio of trace-element in sedimentary rocks to that of upper crust shows gentle character. All the above features indicate that these sedimentary rocks were slowly deposited under weakly active tectonic setting. They are sediments typical for passive continental margin and active continental margin. It is suggested that material source of clastic sediments of Quantou Formation, Cretaceous in Qing-an area, Songliao basin was originated from Hercynian granite of Zhangguangchai Mountain, and the granite was originated from upper crust. (authors)

Petrologic and petrophysical evaluation of the Dallas Center Structure, Iowa, for compressed air energy storage in the Mount Simon Sandstone.

Energy Technology Data Exchange (ETDEWEB)

Heath, Jason E.; Bauer, Stephen J.; Broome, Scott Thomas; Dewers, Thomas A.; Rodriguez, Mark A

2013-03-01

The Iowa Stored Energy Plant Agency selected a geologic structure at Dallas Center, Iowa, for evaluation of subsurface compressed air energy storage. The site was rejected due to lower-than-expected and heterogeneous permeability of the target reservoir, lower-than-desired porosity, and small reservoir volume. In an initial feasibility study, permeability and porosity distributions of flow units for the nearby Redfield gas storage field were applied as analogue values for numerical modeling of the Dallas Center Structure. These reservoir data, coupled with an optimistic reservoir volume, produced favorable results. However, it was determined that the Dallas Center Structure cannot be simplified to four zones of high, uniform permeabilities. Updated modeling using field and core data for the site provided unfavorable results for air fill-up. This report presents Sandia National Laboratories petrologic and petrophysical analysis of the Dallas Center Structure that aids in understanding why the site was not suitable for gas storage.

Preliminary Monthly Climatological Summaries

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary Local Climatological Data, recorded since 1970 on Weather Burean Form 1030 and then National Weather Service Form F-6. The preliminary climate data pages...

Ignorability in Statistical and Probabilistic Inference

DEFF Research Database (Denmark)

Jaeger, Manfred

2005-01-01

When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed...

Quasi-Experimental Designs for Causal Inference

Science.gov (United States)

Kim, Yongnam; Steiner, Peter

2016-01-01

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Inferring probabilistic stellar rotation periods using Gaussian processes

Science.gov (United States)

Angus, Ruth; Morton, Timothy; Aigrain, Suzanne; Foreman-Mackey, Daniel; Rajpaul, Vinesh

2018-02-01

Variability in the light curves of spotted, rotating stars is often non-sinusoidal and quasi-periodic - spots move on the stellar surface and have finite lifetimes, causing stellar flux variations to slowly shift in phase. A strictly periodic sinusoid therefore cannot accurately model a rotationally modulated stellar light curve. Physical models of stellar surfaces have many drawbacks preventing effective inference, such as highly degenerate or high-dimensional parameter spaces. In this work, we test an appropriate effective model: a Gaussian Process with a quasi-periodic covariance kernel function. This highly flexible model allows sampling of the posterior probability density function of the periodic parameter, marginalizing over the other kernel hyperparameters using a Markov Chain Monte Carlo approach. To test the effectiveness of this method, we infer rotation periods from 333 simulated stellar light curves, demonstrating that the Gaussian process method produces periods that are more accurate than both a sine-fitting periodogram and an autocorrelation function method. We also demonstrate that it works well on real data, by inferring rotation periods for 275 Kepler stars with previously measured periods. We provide a table of rotation periods for these and many more, altogether 1102 Kepler objects of interest, and their posterior probability density function samples. Because this method delivers posterior probability density functions, it will enable hierarchical studies involving stellar rotation, particularly those involving population modelling, such as inferring stellar ages, obliquities in exoplanet systems, or characterizing star-planet interactions. The code used to implement this method is available online.

Dopamine, reward learning, and active inference.

Science.gov (United States)

FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl

2015-01-01

Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

Inferences on Children’s Reading Groups

Directory of Open Access Journals (Sweden)

Javier González García

2009-05-01

Full Text Available This article focuses on the non-literal information of a text, which can be inferred from key elements or clues offered by the text itself. This kind of text is called implicit text or inference, due to the thinking process that it stimulates. The explicit resources that lead to information retrieval are related to others of implicit information, which have increased their relevance. In this study, during two courses, how two teachers interpret three stories and how they establish a debate dividing the class into three student groups, was analyzed. The sample was formed by two classes of two urban public schools of Burgos capital (Spain, and two of public schools of Tampico (Mexico. This allowed us to observe an increasing percentage value of the group focused in text comprehension, and a lesser percentage of the group perceiving comprehension as a secondary objective.

Completion is an Instance of Abstract Canonical System Inference

OpenAIRE

Burel , Guillaume; Kirchner , Claude

2006-01-01

http://www.springerlink.com/content/u222753gl333221p/; Abstract canonical systems and inference (ACSI) were introduced to formalize the intuitive notions of good proof and good inference appearing typically in first-order logic or in Knuth-Bendix like completion procedures. Since this abstract framework is intended to be generic, it is of fundamental interest to show its adequacy to represent the main systems of interest. This has been done for ground completion (where all equational axioms a...

On Thinking First and Responding Fast: Flexibility in Social Inference Processes.

Science.gov (United States)

Krull, Douglas S.; Dill, Jody C.

1996-01-01

Investigates the order in which dispositional and situational information is considered. Results indicate that perceivers are flexible in their inference processes: they are able to draw either dispositional or situational inferences initially. Greater understanding of the mechanisms and determinants of social judgments has important implications…

Network Model-Assisted Inference from Respondent-Driven Sampling Data

Science.gov (United States)

Gile, Krista J.; Handcock, Mark S.

2015-01-01

Summary Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population. PMID:26640328

Inferring Human Mobility from Sparse Low Accuracy Mobile Sensing Data

DEFF Research Database (Denmark)

Cuttone, Andrea; Jørgensen, Sune Lehmann; Larsen, Jakob Eg

2014-01-01

Understanding both collective and personal human mobility is a central topic in Computational Social Science. Smartphone sensing data is emerging as a promising source for studying human mobility. However, most literature focuses on high-precision GPS positioning and high-frequency sampling, which...... is not always feasible in a longitudinal study or for everyday applications because location sensing has a high battery cost. In this paper we study the feasibility of inferring human mobility from sparse, low accuracy mobile sensing data. We validate our results using participants' location diaries......, and analyze the inferred geographical networks, the time spent at different places, and the number of unique places over time. Our results suggest that low resolution data allows accurate inference of human mobility patterns....

Inference in hybrid Bayesian networks

DEFF Research Database (Denmark)

Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael

2009-01-01

Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....

Bayesian inference for Markov jump processes with informative observations.

Science.gov (United States)

Golightly, Andrew; Wilkinson, Darren J

2015-04-01

In this paper we consider the problem of parameter inference for Markov jump process (MJP) representations of stochastic kinetic models. Since transition probabilities are intractable for most processes of interest yet forward simulation is straightforward, Bayesian inference typically proceeds through computationally intensive methods such as (particle) MCMC. Such methods ostensibly require the ability to simulate trajectories from the conditioned jump process. When observations are highly informative, use of the forward simulator is likely to be inefficient and may even preclude an exact (simulation based) analysis. We therefore propose three methods for improving the efficiency of simulating conditioned jump processes. A conditioned hazard is derived based on an approximation to the jump process, and used to generate end-point conditioned trajectories for use inside an importance sampling algorithm. We also adapt a recently proposed sequential Monte Carlo scheme to our problem. Essentially, trajectories are reweighted at a set of intermediate time points, with more weight assigned to trajectories that are consistent with the next observation. We consider two implementations of this approach, based on two continuous approximations of the MJP. We compare these constructs for a simple tractable jump process before using them to perform inference for a Lotka-Volterra system. The best performing construct is used to infer the parameters governing a simple model of motility regulation in Bacillus subtilis.

Inference of directional selection and mutation parameters assuming equilibrium.

Science.gov (United States)

Vogl, Claus; Bergman, Juraj

2015-12-01

In a classical study, Wright (1931) proposed a model for the evolution of a biallelic locus under the influence of mutation, directional selection and drift. He derived the equilibrium distribution of the allelic proportion conditional on the scaled mutation rate, the mutation bias and the scaled strength of directional selection. The equilibrium distribution can be used for inference of these parameters with genome-wide datasets of "site frequency spectra" (SFS). Assuming that the scaled mutation rate is low, Wright's model can be approximated by a boundary-mutation model, where mutations are introduced into the population exclusively from sites fixed for the preferred or unpreferred allelic states. With the boundary-mutation model, inference can be partitioned: (i) the shape of the SFS distribution within the polymorphic region is determined by random drift and directional selection, but not by the mutation parameters, such that inference of the selection parameter relies exclusively on the polymorphic sites in the SFS; (ii) the mutation parameters can be inferred from the amount of polymorphic and monomorphic preferred and unpreferred alleles, conditional on the selection parameter. Herein, we derive maximum likelihood estimators for the mutation and selection parameters in equilibrium and apply the method to simulated SFS data as well as empirical data from a Madagascar population of Drosophila simulans. Copyright © 2015 Elsevier Inc. All rights reserved.

Inferring uncertainty from interval estimates: Effects of alpha level and numeracy

Directory of Open Access Journals (Sweden)

Luke F. Rinne

2013-05-01

Full Text Available Interval estimates are commonly used to descriptively communicate the degree of uncertainty in numerical values. Conventionally, low alpha levels (e.g., .05 ensure a high probability of capturing the target value between interval endpoints. Here, we test whether alpha levels and individual differences in numeracy influence distributional inferences. In the reported experiment, participants received prediction intervals for fictitious towns' annual rainfall totals (assuming approximately normal distributions. Then, participants estimated probabilities that future totals would be captured within varying margins about the mean, indicating the approximate shapes of their inferred probability distributions. Results showed that low alpha levels (vs. moderate levels; e.g., .25 more frequently led to inferences of over-dispersed approximately normal distributions or approximately uniform distributions, reducing estimate accuracy. Highly numerate participants made more accurate estimates overall, but were more prone to inferring approximately uniform distributions. These findings have important implications for presenting interval estimates to various audiences.

Processing inferences at the semantics/pragmatics frontier: disjunctions and free choice.

Science.gov (United States)

Chemla, Emmanuel; Bott, Lewis

2014-03-01

Linguistic inferences have traditionally been studied and categorized in several categories, such as entailments, implicatures or presuppositions. This typology is mostly based on traditional linguistic means, such as introspective judgments about phrases occurring in different constructions, in different conversational contexts. More recently, the processing properties of these inferences have also been studied (see, e.g., recent work showing that scalar implicatures is a costly phenomenon). Our focus is on free choice permission, a phenomenon by which conjunctive inferences are unexpectedly added to disjunctive sentences. For instance, a sentence such as "Mary is allowed to eat an ice-cream or a cake" is normally understood as granting permission both for eating an ice-cream and for eating a cake. We provide data from four processing studies, which show that, contrary to arguments coming from the theoretical literature, free choice inferences are different from scalar implicatures. Copyright © 2013 Elsevier B.V. All rights reserved.

Inferring facts from fiction: reading correct and incorrect information affects memory for related information.

Science.gov (United States)

Butler, Andrew C; Dennis, Nancy A; Marsh, Elizabeth J

2012-07-01

People can acquire both true and false knowledge about the world from fictional stories. The present study explored whether the benefits and costs of learning about the world from fictional stories extend beyond memory for directly stated pieces of information. Of interest was whether readers would use correct and incorrect story references to make deductive inferences about related information in the story, and then integrate those inferences into their knowledge bases. Participants read stories containing correct, neutral, and misleading references to facts about the world; each reference could be combined with another reference that occurred in a later sentence to make a deductive inference. Later they answered general knowledge questions that tested for these deductive inferences. The results showed that participants generated and retained the deductive inferences regardless of whether the inferences were consistent or inconsistent with world knowledge, and irrespective of whether the references were placed consecutively in the text or separated by many sentences. Readers learn more than what is directly stated in stories; they use references to the real world to make both correct and incorrect inferences that are integrated into their knowledge bases.

Emotional inferences by pragmatics

OpenAIRE

Iza-Miqueleiz, Mauricio

2017-01-01

It has for long been taken for granted that, along the course of reading a text, world knowledge is often required in order to establish coherent links between sentences (McKoon & Ratcliff 1992, Iza & Ezquerro 2000). The content grasped from a text turns out to be strongly dependent upon the reader’s additional knowledge that allows a coherent interpretation of the text as a whole. The world knowledge directing the inference may be of distinctive nature. Gygax et al. (2007) showed that m...

Intelligent machines in the twenty-first century: foundations of inference and inquiry.

Science.gov (United States)

Knuth, Kevin H

2003-12-15

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have

Towards Bayesian Inference of the Fast-Ion Distribution Function

DEFF Research Database (Denmark)

Stagner, L.; Heidbrink, W.W.; Salewski, Mirko

2012-01-01

sensitivity of the measurements are incorporated into Bayesian likelihood probabilities, while prior probabilities enforce physical constraints. As an initial step, this poster uses Bayesian statistics to infer the DIII-D electron density profile from multiple diagnostic measurements. Likelihood functions....... However, when theory and experiment disagree (for one or more diagnostics), it is unclear how to proceed. Bayesian statistics provides a framework to infer the DF, quantify errors, and reconcile discrepant diagnostic measurements. Diagnostic errors and ``weight functions" that describe the phase space...

IERIAS: inference engine for reactor accident diagnostic system using knowledge engineering technique

International Nuclear Information System (INIS)

Yokobayashi, Masao; Yoshida, Kazuo; Kohsaka, Atsuo; Yamamoto, Minoru.

1984-11-01

This report describes an inference engine IERIAS which has been devoloped for a diagnostic system to identify the cause and type of an abnormal transient of a reactor plant. This system using knowledge engineering technique consists of a knowledge base and an inference engine. The inference engine IERIAS is designed so as to treat time-varying data of a plant. The major features of IERIAS are ; (1) histroy of transients can be treated, (2) knowledge base can be divided into some knowledge units, (3) program language UTILISP is used which is suitable for symbolic data manipulation. Inference was made using IERIAS with a knowledge base which was created from simulated results of various transients by a PWR plant simulator. The results showed a good applicability of IERIAS for reactor diagnosis. (author)

Inference in partially identified models with many moment inequalities using Lasso

DEFF Research Database (Denmark)

Bugni, Federico A.; Caner, Mehmet; Kock, Anders Bredahl

This paper considers the problem of inference in a partially identified moment (in)equality model with possibly many moment inequalities. Our contribution is to propose a novel two-step new inference method based on the combination of two ideas. On the one hand, our test statistic and critical...

Assessing children's inference generation: what do tests of reading comprehension measure?

Science.gov (United States)

Bowyer-Crane, Claudine; Snowling, Margaret J

2005-06-01

Previous research suggests that children with specific comprehension difficulties have problems with the generation of inferences. This raises important questions as to whether poor comprehenders have poor comprehension skills generally, or whether their problems are confined to specific inference types. The main aims of the study were (a) using two commonly used tests of reading comprehension to classify the questions requiring the generation of inferences, and (b) to investigate the relative performance of skilled and less-skilled comprehenders on questions tapping different inference types. The performance of 10 poor comprehenders (mean age 110.06 months) was compared with the performance of 10 normal readers (mean age 112.78 months) on two tests of reading comprehension. A qualitative analysis of the NARA II (form 1) and the WORD comprehension subtest was carried out. Participants were then administered the NARA II, WORD comprehension subtest and a test of non-word reading. The NARA II was heavily reliant on the generation of knowledge-based inferences, while the WORD comprehension subtest was biased towards the retention of literal information. Children identified by the NARA II as having comprehension difficulties performed in the normal range on the WORD comprehension subtests. Further, children with comprehension difficulties performed poorly on questions requiring the generation of knowledge-based and elaborative inferences. However, they were able to answer questions requiring attention to literal information or use of cohesive devices at a level comparable to normal readers. Different reading tests tap different types of inferencing skills. Lessskilled comprehenders have particular difficulty applying real-world knowledge to a text during reading, and this has implications for the formulation of effective intervention strategies.

Generating inferences from knowledge structures based on general automata

Energy Technology Data Exchange (ETDEWEB)

Koenig, E C

1983-01-01

The author shows that the model for knowledge structures for computers based on general automata accommodates procedures for establishing inferences. Algorithms are presented which generate inferences as output of a computer when its sentence input names appropriate knowledge elements contained in an associated knowledge structure already stored in the memory of the computer. The inferences are found to have either a single graph tuple or more than one graph tuple of associated knowledge. Six algorithms pertain to a single graph tuple and a seventh pertains to more than one graph tuple of associated knowledge. A named term is either the automaton, environment, auxiliary receptor, principal receptor, auxiliary effector, or principal effector. The algorithm pertaining to more than one graph tuple requires that the input sentence names the automaton, transformation response, and environment of one of the tuples of associated knowledge in a sequence of tuples. Interaction with the computer may be either in a conversation or examination mode. The algorithms are illustrated by an example. 13 references.

Evidence Accumulation and Change Rate Inference in Dynamic Environments.

Science.gov (United States)

Radillo, Adrian E; Veliz-Cuba, Alan; Josić, Krešimir; Kilpatrick, Zachary P

2017-06-01

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible change point counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation-based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments and map this computation to reduced models that perform inference using plausible neural mechanisms.

An emergent approach to analogical inference

Science.gov (United States)

Thibodeau, Paul H.; Flusberg, Stephen J.; Glick, Jeremy J.; Sternberg, Daniel A.

2013-03-01

In recent years, a growing number of researchers have proposed that analogy is a core component of human cognition. According to the dominant theoretical viewpoint, analogical reasoning requires a specific suite of cognitive machinery, including explicitly coded symbolic representations and a mapping or binding mechanism that operates over these representations. Here we offer an alternative approach: we find that analogical inference can emerge naturally and spontaneously from a relatively simple, error-driven learning mechanism without the need to posit any additional analogy-specific machinery. The results also parallel findings from the developmental literature on analogy, demonstrating a shift from an initial reliance on surface feature similarity to the use of relational similarity later in training. Variants of the model allow us to consider and rule out alternative accounts of its performance. We conclude by discussing how these findings can potentially refine our understanding of the processes that are required to perform analogical inference.

Dopamine, reward learning, and active inference

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Thomas eFitzgerald

2015-11-01

Full Text Available Temporal difference learning models propose phasic dopamine signalling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behaviour. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

The formation of FeO-rich pyroxene and enstatite in unequilibrated enstatite chondrites: A petrologic-trace element (SIMS) study

Science.gov (United States)

Weisberg, M. K.; Prinz, M.; Fogel, R. A.; Shimizu, N.

1993-01-01

Enstatite (En) chondrites record the most reducing conditions known in the early solar system. Their oxidation state may be the result of condensation in a nebular region having an enhanced C/O ratio, reduction of more oxidized materials in a reducing nebula, reduction during metamorphic reheating in a parent body, or a combination of these events. The presence of more oxidized Fe-rich silicates, two types of En (distinguished by red and blue CL), and the juxtaposition of FeO-rich pyroxenes (Fe-pyx) surrounded by blue En (enstatite) in the UEC's (unequilibrated enstatite chondrites) is intriguing and led to the examination of the question of enstatite chondrite formation. Previously, data was presented on the petrologic-geochemical characteristics of the Fe-pyx and coexisting red and blue En. Here minor and trace element abundances (determined by ion probe-SIMS) on these three types of pyroxenes are reported on in the following meteorites: Kota Kota and LEW87223 (EH3), MAC88136 (EL3), St. Marks (EH4), and Hvittis (EL6). More data are currently being collected.

Kernel learning at the first level of inference.

Science.gov (United States)

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

PhySIC_IST: cleaning source trees to infer more informative supertrees.

Science.gov (United States)

Scornavacca, Celine; Berry, Vincent; Lefort, Vincent; Douzery, Emmanuel J P; Ranwez, Vincent

2008-10-04

Supertree methods combine phylogenies with overlapping sets of taxa into a larger one. Topological conflicts frequently arise among source trees for methodological or biological reasons, such as long branch attraction, lateral gene transfers, gene duplication/loss or deep gene coalescence. When topological conflicts occur among source trees, liberal methods infer supertrees containing the most frequent alternative, while veto methods infer supertrees not contradicting any source tree, i.e. discard all conflicting resolutions. When the source trees host a significant number of topological conflicts or have a small taxon overlap, supertree methods of both kinds can propose poorly resolved, hence uninformative, supertrees. To overcome this problem, we propose to infer non-plenary supertrees, i.e. supertrees that do not necessarily contain all the taxa present in the source trees, discarding those whose position greatly differs among source trees or for which insufficient information is provided. We detail a variant of the PhySIC veto method called PhySIC_IST that can infer non-plenary supertrees. PhySIC_IST aims at inferring supertrees that satisfy the same appealing theoretical properties as with PhySIC, while being as informative as possible under this constraint. The informativeness of a supertree is estimated using a variation of the CIC (Cladistic Information Content) criterion, that takes into account both the presence of multifurcations and the absence of some taxa. Additionally, we propose a statistical preprocessing step called STC (Source Trees Correction) to correct the source trees prior to the supertree inference. STC is a liberal step that removes the parts of each source tree that significantly conflict with other source trees. Combining STC with a veto method allows an explicit trade-off between veto and liberal approaches, tuned by a single parameter.Performing large-scale simulations, we observe that STC+PhySIC_IST infers much more informative

Inference of gene regulatory networks from time series by Tsallis entropy

Directory of Open Access Journals (Sweden)

de Oliveira Evaldo A

2011-05-01

Full Text Available Abstract Background The inference of gene regulatory networks (GRNs from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information, a new criterion function is here proposed. Results In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 �

A neuro-fuzzy inference system for sensor monitoring

International Nuclear Information System (INIS)

Na, Man Gyun

2001-01-01

A neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods has been developed to monitor the relevant sensor using the information of other sensors. The paramters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors

Bayesian inference on genetic merit under uncertain paternity

Directory of Open Access Journals (Sweden)

Tempelman Robert J

2003-09-01

Full Text Available Abstract A hierarchical animal model was developed for inference on genetic merit of livestock with uncertain paternity. Fully conditional posterior distributions for fixed and genetic effects, variance components, sire assignments and their probabilities are derived to facilitate a Bayesian inference strategy using MCMC methods. We compared this model to a model based on the Henderson average numerator relationship (ANRM in a simulation study with 10 replicated datasets generated for each of two traits. Trait 1 had a medium heritability (h2 for each of direct and maternal genetic effects whereas Trait 2 had a high h2 attributable only to direct effects. The average posterior probabilities inferred on the true sire were between 1 and 10% larger than the corresponding priors (the inverse of the number of candidate sires in a mating pasture for Trait 1 and between 4 and 13% larger than the corresponding priors for Trait 2. The predicted additive and maternal genetic effects were very similar using both models; however, model choice criteria (Pseudo Bayes Factor and Deviance Information Criterion decisively favored the proposed hierarchical model over the ANRM model.

Grouping preprocess for haplotype inference from SNP and CNV data

International Nuclear Information System (INIS)

Shindo, Hiroyuki; Chigira, Hiroshi; Nagaoka, Tomoyo; Inoue, Masato; Kamatani, Naoyuki

2009-01-01

The method of statistical haplotype inference is an indispensable technique in the field of medical science. The authors previously reported Hardy-Weinberg equilibrium-based haplotype inference that could manage single nucleotide polymorphism (SNP) data. We recently extended the method to cover copy number variation (CNV) data. Haplotype inference from mixed data is important because SNPs and CNVs are occasionally in linkage disequilibrium. The idea underlying the proposed method is simple, but the algorithm for it needs to be quite elaborate to reduce the calculation cost. Consequently, we have focused on the details on the algorithm in this study. Although the main advantage of the method is accuracy, in that it does not use any approximation, its main disadvantage is still the calculation cost, which is sometimes intractable for large data sets with missing values.

Grouping preprocess for haplotype inference from SNP and CNV data

Energy Technology Data Exchange (ETDEWEB)

Shindo, Hiroyuki; Chigira, Hiroshi; Nagaoka, Tomoyo; Inoue, Masato [Department of Electrical Engineering and Bioscience, School of Advanced Science and Engineering, Waseda University, 3-4-1, Okubo, Shinjuku-ku, Tokyo 169-8555 (Japan); Kamatani, Naoyuki, E-mail: masato.inoue@eb.waseda.ac.j [Institute of Rheumatology, Tokyo Women' s Medical University, 10-22, Kawada-cho, Shinjuku-ku, Tokyo 162-0054 (Japan)

2009-12-01

The method of statistical haplotype inference is an indispensable technique in the field of medical science. The authors previously reported Hardy-Weinberg equilibrium-based haplotype inference that could manage single nucleotide polymorphism (SNP) data. We recently extended the method to cover copy number variation (CNV) data. Haplotype inference from mixed data is important because SNPs and CNVs are occasionally in linkage disequilibrium. The idea underlying the proposed method is simple, but the algorithm for it needs to be quite elaborate to reduce the calculation cost. Consequently, we have focused on the details on the algorithm in this study. Although the main advantage of the method is accuracy, in that it does not use any approximation, its main disadvantage is still the calculation cost, which is sometimes intractable for large data sets with missing values.

Experimental and petrological constraints on long-term magma dynamics and post-climactic eruptions at the Cerro Galán caldera system, NW Argentina

Science.gov (United States)

Grocke, Stephanie B.; Andrews, Benjamin J.; de Silva, Shanaka L.

2017-11-01

Cerro Galán in NW Argentina records > 3.5 Myr of magmatic evolution of a major resurgent caldera complex. Beginning at 5.72 Ma, nine rhyodacitic ignimbrites (68-71 wt% SiO2) with a combined minimum volume of > 1200 km3 (Dense Rock Equivalent; DRE) have been erupted. The youngest of those ignimbrites is the eponymous, geochemically homogenous, caldera-forming 2.08 ± 0.02 Ma Cerro Galán Ignimbrite (CGI; > 630 km3 DRE). Following this climactic supereruption, structural and magmatic resurgence led to the formation of a resurgent dome and post-climactic lava domes and their associated pyroclastic deposits. A clear transition from amphibole to sanidine-bearing magmas occurred during the evolution of Cerro Galán and is inferred to represent a shallowing of the magma system. We test this hypothesis here using experimental phase equilibria. We conducted a series of phase equilibria experiments on the post-climactic dome lithologies under H2O-saturated conditions using cold seal Waspaloy pressure vessels with an intrinsic log fO2 of NNO + 1 ± 0.5 across a temperature-pressure range of 750-900 °C and 50-200 MPa (PH2O = Ptotal), respectively. Petrologic and geochemical analysis of the post-climactic lithologies shows that the natural phase assemblage (plagioclase + quartz + biotite + sanidine + Fe-Ti oxides ± apatite ± zircon) is stable at history of Cerro Galán is informed through a detailed investigation of the textural differences among the post-climactic dome lithologies, and a comparison of those textures with previously published decompression experiments. These suggest that the highly vesiculated, pumiceous clasts with rare microlites represent magma stored within the core of the lava dome that decompressed relatively rapidly (0.003-0.0003 MPa s-1) and evolved via closed system degassing. Resulting over-pressure of the dome may have triggered superficial explosion. In contrast, dense clasts with abundant crystalline silica precipitates represent more typical

On Quantum Statistical Inference, II

OpenAIRE

Barndorff-Nielsen, O. E.; Gill, R. D.; Jupp, P. E.

2003-01-01

Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, theoretical developments in the theory of quantum measurements have brought the basic mathematical framework for the probability calculations much closer to that of classical probability theory. The present paper reviews this field and proposes and inte...

Identifikasi Gangguan Neurologis Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS

Directory of Open Access Journals (Sweden)

Jani Kusanti

2015-07-01

Abstract             The use of Adaptive Neuro Fuzzy Inference System (ANFIS methods in the process of identifying one of neurological disorders in the head, known in medical terms ischemic stroke from the ct scan of the head in order to identify the location of ischemic stroke. The steps are performed in the extraction process of identifying, among others, the image of the ct scan of the head by using a histogram. Enhanced image of the intensity histogram image results using Otsu threshold to obtain results pixels rated 1 related to the object while pixel rated 0 associated with the measurement background. The result used for image clustering process, to process image clusters used fuzzy c-mean (FCM clustering result is a row of the cluster center, the results of the data used to construct a fuzzy inference system (FIS. Fuzzy inference system applied is fuzzy inference model of Takagi-Sugeno-Kang. In this study ANFIS is used to optimize the results of the determination of the location of the blockage ischemic stroke. Used recursive least squares estimator (RLSE for learning. RMSE results obtained in the training process of 0.0432053, while in the process of generated test accuracy rate of 98.66%   Keywords— Stroke Ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE

F-OWL: An Inference Engine for Semantic Web

Science.gov (United States)

Zou, Youyong; Finin, Tim; Chen, Harry

2004-01-01

Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.

Inference with constrained hidden Markov models in PRISM

DEFF Research Database (Denmark)

Christiansen, Henning; Have, Christian Theil; Lassen, Ole Torp

2010-01-01

A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. De......_different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment.......A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference...

AD-LIBS: inferring ancestry across hybrid genomes using low-coverage sequence data.

Science.gov (United States)

Schaefer, Nathan K; Shapiro, Beth; Green, Richard E

2017-04-04

Inferring the ancestry of each region of admixed individuals' genomes is useful in studies ranging from disease gene mapping to speciation genetics. Current methods require high-coverage genotype data and phased reference panels, and are therefore inappropriate for many data sets. We present a software application, AD-LIBS, that uses a hidden Markov model to infer ancestry across hybrid genomes without requiring variant calling or phasing. This approach is useful for non-model organisms and in cases of low-coverage data, such as ancient DNA. We demonstrate the utility of AD-LIBS with synthetic data. We then use AD-LIBS to infer ancestry in two published data sets: European human genomes with Neanderthal ancestry and brown bear genomes with polar bear ancestry. AD-LIBS correctly infers 87-91% of ancestry in simulations and produces ancestry maps that agree with published results and global ancestry estimates in humans. In brown bears, we find more polar bear ancestry than has been published previously, using both AD-LIBS and an existing software application for local ancestry inference, HAPMIX. We validate AD-LIBS polar bear ancestry maps by recovering a geographic signal within bears that mirrors what is seen in SNP data. Finally, we demonstrate that AD-LIBS is more effective than HAPMIX at inferring ancestry when preexisting phased reference data are unavailable and genomes are sequenced to low coverage. AD-LIBS is an effective tool for ancestry inference that can be used even when few individuals are available for comparison or when genomes are sequenced to low coverage. AD-LIBS is therefore likely to be useful in studies of non-model or ancient organisms that lack large amounts of genomic DNA. AD-LIBS can therefore expand the range of studies in which admixture mapping is a viable tool.

Cultural effects on the association between election outcomes and face-based trait inferences

Science.gov (United States)

Adolphs, Ralph; Alvarez, R. Michael

2017-01-01

How competent a politician looks, as assessed in the laboratory, is correlated with whether the politician wins in real elections. This finding has led many to investigate whether the association between candidate appearances and election outcomes transcends cultures. However, these studies have largely focused on European countries and Caucasian candidates. To the best of our knowledge, there are only four cross-cultural studies that have directly investigated how face-based trait inferences correlate with election outcomes across Caucasian and Asian cultures. These prior studies have provided some initial evidence regarding cultural differences, but methodological problems and inconsistent findings have complicated our understanding of how culture mediates the effects of candidate appearances on election outcomes. Additionally, these four past studies have focused on positive traits, with a relative neglect of negative traits, resulting in an incomplete picture of how culture may impact a broader range of trait inferences. To study Caucasian-Asian cultural effects with a more balanced experimental design, and to explore a more complete profile of traits, here we compared how Caucasian and Korean participants’ inferences of positive and negative traits correlated with U.S. and Korean election outcomes. Contrary to previous reports, we found that inferences of competence (made by participants from both cultures) correlated with both U.S. and Korean election outcomes. Inferences of open-mindedness and threat, two traits neglected in previous cross-cultural studies, were correlated with Korean but not U.S. election outcomes. This differential effect was found in trait judgments made by both Caucasian and Korean participants. Interestingly, the faster the participants made face-based trait inferences, the more strongly those inferences were correlated with real election outcomes. These findings provide new insights into cultural effects and the difficult question of

Cultural effects on the association between election outcomes and face-based trait inferences.

Science.gov (United States)

Lin, Chujun; Adolphs, Ralph; Alvarez, R Michael

2017-01-01

How competent a politician looks, as assessed in the laboratory, is correlated with whether the politician wins in real elections. This finding has led many to investigate whether the association between candidate appearances and election outcomes transcends cultures. However, these studies have largely focused on European countries and Caucasian candidates. To the best of our knowledge, there are only four cross-cultural studies that have directly investigated how face-based trait inferences correlate with election outcomes across Caucasian and Asian cultures. These prior studies have provided some initial evidence regarding cultural differences, but methodological problems and inconsistent findings have complicated our understanding of how culture mediates the effects of candidate appearances on election outcomes. Additionally, these four past studies have focused on positive traits, with a relative neglect of negative traits, resulting in an incomplete picture of how culture may impact a broader range of trait inferences. To study Caucasian-Asian cultural effects with a more balanced experimental design, and to explore a more complete profile of traits, here we compared how Caucasian and Korean participants' inferences of positive and negative traits correlated with U.S. and Korean election outcomes. Contrary to previous reports, we found that inferences of competence (made by participants from both cultures) correlated with both U.S. and Korean election outcomes. Inferences of open-mindedness and threat, two traits neglected in previous cross-cultural studies, were correlated with Korean but not U.S. election outcomes. This differential effect was found in trait judgments made by both Caucasian and Korean participants. Interestingly, the faster the participants made face-based trait inferences, the more strongly those inferences were correlated with real election outcomes. These findings provide new insights into cultural effects and the difficult question of

Inference of population splits and mixtures from genome-wide allele frequency data.

Directory of Open Access Journals (Sweden)

Joseph K Pickrell

Full Text Available Many aspects of the historical relationships between populations in a species are reflected in genetic data. Inferring these relationships from genetic data, however, remains a challenging task. In this paper, we present a statistical model for inferring the patterns of population splits and mixtures in multiple populations. In our model, the sampled populations in a species are related to their common ancestor through a graph of ancestral populations. Using genome-wide allele frequency data and a Gaussian approximation to genetic drift, we infer the structure of this graph. We applied this method to a set of 55 human populations and a set of 82 dog breeds and wild canids. In both species, we show that a simple bifurcating tree does not fully describe the data; in contrast, we infer many migration events. While some of the migration events that we find have been detected previously, many have not. For example, in the human data, we infer that Cambodians trace approximately 16% of their ancestry to a population ancestral to other extant East Asian populations. In the dog data, we infer that both the boxer and basenji trace a considerable fraction of their ancestry (9% and 25%, respectively to wolves subsequent to domestication and that East Asian toy breeds (the Shih Tzu and the Pekingese result from admixture between modern toy breeds and "ancient" Asian breeds. Software implementing the model described here, called TreeMix, is available at http://treemix.googlecode.com.

ShinyKGode: an Interactive Application for ODE Parameter Inference Using Gradient Matching.

Science.gov (United States)

Wandy, Joe; Niu, Mu; Giurghita, Diana; Daly, Rónán; Rogers, Simon; Husmeier, Dirk

2018-02-27

Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to be inferred from the data. Approximate parameter inference methods based on gradient matching (which do not require performing computationally expensive numerical integration of the ODEs) have been getting popular in recent years, but many implementations are difficult to run without expert knowledge. Here we introduce ShinyKGode, an interactive web application to perform fast parameter inference on ODEs using gradient matching. ShinyKGode can be used to infer ODE parameters on simulated and observed data using gradient matching. Users can easily load their own models in Systems Biology Markup Language format, and a set of pre-defined ODE benchmark models are provided in the application. Inferred parameters are visualised alongside diagnostic plots to assess convergence. The R package for ShinyKGode can be installed through the Comprehensive R Archive Network (CRAN). Installation instructions, as well as tutorial videos and source code are available at https://joewandy.github.io/shinyKGode. dirk.husmeier@glasgow.ac.uk. None.

Online Emotional Inferences in Written and Auditory Texts: A Study with Children and Adults

Science.gov (United States)

Diergarten, Anna Katharina; Nieding, Gerhild

2016-01-01

Emotional inferences are conclusions that a reader draws about the emotional state of a story's protagonist. In this study, we examined whether children and adults draw emotional inferences while reading short stories or listening to an aural presentation of short stories. We used an online method that assesses inferences during reading with a…

Ontological Constraints in Children's Inductive Inferences: Evidence From a Comparison of Inferences Within Animals and Vehicles

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Andrzej Tarlowski

2018-04-01

Full Text Available There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects.

Inference of a Nonlinear Stochastic Model of the Cardiorespiratory Interaction

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Smelyanskiy, V. N.; Luchinsky, D. G.; Stefanovska, A.; McClintock, P. V.

2005-03-01

We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set of polynomial basis functions representing the nonlinear force governing system oscillations. The strength and direction of coupling and noise intensity are simultaneously inferred from a univariate blood pressure signal. Our new inference technique does not require extensive global optimization, and it is applicable to a wide range of complex dynamical systems subject to noise.

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

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Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

Inferring the conservative causal core of gene regulatory networks

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Emmert-Streib Frank

2010-09-01

Full Text Available Abstract Background Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. Results In this paper, we introduce a novel gene regulatory network inference (GRNI algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. Conclusions For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

Inferring the conservative causal core of gene regulatory networks.

Science.gov (United States)

Altay, Gökmen; Emmert-Streib, Frank

2010-09-28

Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

Negativity and positivity effects in person perception and inference: Ability versus morality

NARCIS (Netherlands)

Martijn, A.C.; Spears, R.; van der Pligt, J.; Jakobs, E.

1992-01-01

Examined, in 2 experiments involving 190 undergraduates, negativity and positivity effects in trait inferences and impression formation. In Exp 1, Ss made trait inferences of actors in different behavioral instances. Results support the prediction that negative behavior is more informative for

Gradient matching methods for computational inference in mechanistic models for systems biology: a review and comparative analysis

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Benn eMacdonald

2015-11-01

Full Text Available Parameter inference in mathematical models of biological pathways, expressed as coupled ordinary differential equations (ODEs, is a challenging problem in contemporary systems biology. Conventional methods involve repeatedly solving the ODEs by numerical integration, which is computationally onerous and does not scale up to complex systems. Aimed at reducing the computational costs, new concepts based on gradient matching have recently been proposed in the computational statistics and machine learning literature. In a preliminary smoothing step, the time series data are interpolated; then, in a second step, the parameters of the ODEs are optimised so as to minimise some metric measuring the difference between the slopes of the tangents to the interpolants, and the time derivatives from the ODEs. In this way, the ODEs never have to be solved explicitly. This review provides a concise methodological overview of the current state-of-the-art methods for gradient matching in ODEs, followed by an empirical comparative evaluation based on a set of widely used and representative benchmark data.

A grammar inference approach for predicting kinase specific phosphorylation sites.

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Datta, Sutapa; Mukhopadhyay, Subhasis

2015-01-01

Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner.

A Grammar Inference Approach for Predicting Kinase Specific Phosphorylation Sites

Science.gov (United States)

Datta, Sutapa; Mukhopadhyay, Subhasis

2015-01-01

Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner. PMID:25886273

Reliability of dose volume constraint inference from clinical data

DEFF Research Database (Denmark)

Lutz, C M; Møller, D S; Hoffmann, L

2017-01-01

Dose volume histogram points (DVHPs) frequently serve as dose constraints in radiotherapy treatment planning. An experiment was designed to investigate the reliability of DVHP inference from clinical data for multiple cohort sizes and complication incidence rates. The experimental background...... was radiation pneumonitis in non-small cell lung cancer and the DVHP inference method was based on logistic regression. From 102 NSCLC real-life dose distributions and a postulated DVHP model, an 'ideal' cohort was generated where the most predictive model was equal to the postulated model. A bootstrap...

Paradoxical versus modulated conditional inferences: An explanation from the Stoicism

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Miguel López-Astorga

Full Text Available Abstract According to standard propositional logic, the inferences in which the conditional introduction rule is used are absolutely correct. However, people do not always accept inferences of that kind. Orenes and Johnson-Laird carried out interesting experiments in this way and, based on the general framework of the mental models theory, explained clearly in which cases and under which circumstances such inferences are accepted and rejected. The goals of this paper are both to better understand some aspects of Stoic logic and to check whether or not that very logic can also offer an account on this issue. My conclusions are that, indeed, this later logic can do that, and that the results obtained by Orenes and Johnson-Laird can be explained based on the information that the sources provide on Stoic logic.

Technical Note: How to use Winbugs to infer animal models

DEFF Research Database (Denmark)

Damgaard, Lars Holm

2007-01-01

This paper deals with Bayesian inferences of animal models using Gibbs sampling. First, we suggest a general and efficient method for updating additive genetic effects, in which the computational cost is independent of the pedigree depth and increases linearly only with the size of the pedigree....... Second, we show how this approach can be used to draw inferences from a wide range of animal models using the computer package Winbugs. Finally, we illustrate the approach in a simulation study, in which the data are generated and analyzed using Winbugs according to a linear model with i.i.d errors...... having Student's t distributions. In conclusion, Winbugs can be used to make inferences in small-sized, quantitative, genetic data sets applying a wide range of animal models that are not yet standard in the animal breeding literature...

BIPS-FS preliminary design, miscellaneous notes

International Nuclear Information System (INIS)

1976-01-01

A compendium of flight system preliminary design internal memos and progress report extracts for the Brayton Isotope Power System Preliminary Design Review to be held July 20, 21, and 22, 1975 is presented. The purpose is to bring together those published items which relate only to the preliminary design of the Flight System, Task 2 of Phase I. This preliminary design effort was required to ensure that the Ground Demonstration System will represent the Flight System as closely as possible

45 CFR 150.217 - Preliminary determination.

Science.gov (United States)

2010-10-01

... 45 Public Welfare 1 2010-10-01 2010-10-01 false Preliminary determination. 150.217 Section 150.217... Are Failing To Substantially Enforce HIPAA Requirements § 150.217 Preliminary determination. If, at... designees). (b) Notifies the State of CMS's preliminary determination that the State has failed to...

Design of uav robust autopilot based on adaptive neuro-fuzzy inference system

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Mohand Achour Touat

2008-04-01

Full Text Available  This paper is devoted to the application of adaptive neuro-fuzzy inference systems to the robust control of the UAV longitudinal motion. The adaptive neore-fuzzy inference system model needs to be trained by input/output data. This data were obtained from the modeling of a ”crisp” robust control system. The synthesis of this system is based on the separation theorem, which defines the structure and parameters of LQG-optimal controller, and further - robust optimization of this controller, based on the genetic algorithm. Such design procedure can define the rule base and parameters of fuzzyfication and defuzzyfication algorithms of the adaptive neore-fuzzy inference system controller, which ensure the robust properties of the control system. Simulation of the closed loop control system of UAV longitudinal motion with adaptive neore-fuzzy inference system controller demonstrates high efficiency of proposed design procedure.

Interest, Inferences, and Learning from Texts

Science.gov (United States)

Clinton, Virginia; van den Broek, Paul

2012-01-01

Topic interest and learning from texts have been found to be positively associated with each other. However, the reason for this positive association is not well understood. The purpose of this study is to examine a cognitive process, inference generation, that could explain the positive association between interest and learning from texts. In…

Inferring Stop-Locations from WiFi

DEFF Research Database (Denmark)

Wind, David Kofoed; Sapiezynski, Piotr; Furman, Magdalena Anna

2016-01-01

methods are based exclusively on WiFi data. We study two months of WiFi data collected every two minutes by a smartphone, and infer stop-locations in the form of labelled time-intervals. For this purpose, we investigate two algorithms, both of which scale to large datasets: a greedy approach to select...

Inference and the Introductory Statistics Course

Science.gov (United States)

Pfannkuch, Maxine; Regan, Matt; Wild, Chris; Budgett, Stephanie; Forbes, Sharleen; Harraway, John; Parsonage, Ross

2011-01-01

This article sets out some of the rationale and arguments for making major changes to the teaching and learning of statistical inference in introductory courses at our universities by changing from a norm-based, mathematical approach to more conceptually accessible computer-based approaches. The core problem of the inferential argument with its…

A closer look at the effect of preliminary goodness-of-fit testing for normality for the one-sample t-test.

Science.gov (United States)

Rochon, Justine; Kieser, Meinhard

2011-11-01

Student's one-sample t-test is a commonly used method when inference about the population mean is made. As advocated in textbooks and articles, the assumption of normality is often checked by a preliminary goodness-of-fit (GOF) test. In a paper recently published by Schucany and Ng it was shown that, for the uniform distribution, screening of samples by a pretest for normality leads to a more conservative conditional Type I error rate than application of the one-sample t-test without preliminary GOF test. In contrast, for the exponential distribution, the conditional level is even more elevated than the Type I error rate of the t-test without pretest. We examine the reasons behind these characteristics. In a simulation study, samples drawn from the exponential, lognormal, uniform, Student's t-distribution with 2 degrees of freedom (t(2) ) and the standard normal distribution that had passed normality screening, as well as the ingredients of the test statistics calculated from these samples, are investigated. For non-normal distributions, we found that preliminary testing for normality may change the distribution of means and standard deviations of the selected samples as well as the correlation between them (if the underlying distribution is non-symmetric), thus leading to altered distributions of the resulting test statistics. It is shown that for skewed distributions the excess in Type I error rate may be even more pronounced when testing one-sided hypotheses. ©2010 The British Psychological Society.

A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data

Science.gov (United States)

Gallopin, Mélina; Rau, Andrea; Jaffrézic, Florence

2013-01-01

Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data. PMID:24147011

A feedback framework for protein inference with peptides identified from tandem mass spectra

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Shi Jinhong

2012-11-01

Full Text Available Abstract Background Protein inference is an important computational step in proteomics. There exists a natural nest relationship between protein inference and peptide identification, but these two steps are usually performed separately in existing methods. We believe that both peptide identification and protein inference can be improved by exploring such nest relationship. Results In this study, a feedback framework is proposed to process peptide identification reports from search engines, and an iterative method is implemented to exemplify the processing of Sequest peptide identification reports according to the framework. The iterative method is verified on two datasets with known validity of proteins and peptides, and compared with ProteinProphet and PeptideProphet. The results have shown that not only can the iterative method infer more true positive and less false positive proteins than ProteinProphet, but also identify more true positive and less false positive peptides than PeptideProphet. Conclusions The proposed iterative method implemented according to the feedback framework can unify and improve the results of peptide identification and protein inference.

I know why you voted for Trump: (Over)inferring motives based on choice.

Science.gov (United States)

Barasz, Kate; Kim, Tami; Evangelidis, Ioannis

2018-05-10

People often speculate about why others make the choices they do. This paper investigates how such inferences are formed as a function of what is chosen. Specifically, when observers encounter someone else's choice (e.g., of political candidate), they use the chosen option's attribute values (e.g., a candidate's specific stance on a policy issue) to infer the importance of that attribute (e.g., the policy issue) to the decision-maker. Consequently, when a chosen option has an attribute whose value is extreme (e.g., an extreme policy stance), observers infer-sometimes incorrectly-that this attribute disproportionately motivated the decision-maker's choice. Seven studies demonstrate how observers use an attribute's value to infer its weight-the value-weight heuristic-and identify the role of perceived diagnosticity: more extreme attribute values give observers the subjective sense that they know more about a decision-maker's preferences, and in turn, increase the attribute's perceived importance. The paper explores how this heuristic can produce erroneous inferences and influence broader beliefs about decision-makers. Copyright © 2018 Elsevier B.V. All rights reserved.

A multi-criteria inference approach for anti-desertification management.

Science.gov (United States)

Tervonen, Tommi; Sepehr, Adel; Kadziński, Miłosz

2015-10-01

We propose an approach for classifying land zones into categories indicating their resilience against desertification. Environmental management support is provided by a multi-criteria inference method that derives a set of value functions compatible with the given classification examples, and applies them to define, for the rest of the zones, their possible classes. In addition, a representative value function is inferred to explain the relative importance of the criteria to the stakeholders. We use the approach for classifying 28 administrative regions of the Khorasan Razavi province in Iran into three equilibrium classes: collapsed, transition, and sustainable zones. The model is parameterized with enhanced vegetation index measurements from 2005 to 2012, and 7 other natural and anthropogenic indicators for the status of the region in 2012. Results indicate that grazing density and land use changes are the main anthropogenic factors affecting desertification in Khorasan Razavi. The inference procedure suggests that the classification model is underdetermined in terms of attributes, but the approach itself is promising for supporting the management of anti-desertification efforts. Copyright © 2015 Elsevier Ltd. All rights reserved.

Causal Inference and Explaining Away in a Spiking Network

Science.gov (United States)

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-01-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

Science.gov (United States)

Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

2018-06-01

This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Bayesian inference in probabilistic risk assessment-The current state of the art

International Nuclear Information System (INIS)

Kelly, Dana L.; Smith, Curtis L.

2009-01-01

Markov chain Monte Carlo (MCMC) approaches to sampling directly from the joint posterior distribution of aleatory model parameters have led to tremendous advances in Bayesian inference capability in a wide variety of fields, including probabilistic risk analysis. The advent of freely available software coupled with inexpensive computing power has catalyzed this advance. This paper examines where the risk assessment community is with respect to implementing modern computational-based Bayesian approaches to inference. Through a series of examples in different topical areas, it introduces salient concepts and illustrates the practical application of Bayesian inference via MCMC sampling to a variety of important problems

Comparison of Urban Human Movements Inferring from Multi-Source Spatial-Temporal Data

Science.gov (United States)

Cao, Rui; Tu, Wei; Cao, Jinzhou; Li, Qingquan

2016-06-01

The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ) level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.

COMPARISON OF URBAN HUMAN MOVEMENTS INFERRING FROM MULTI-SOURCE SPATIAL-TEMPORAL DATA

Directory of Open Access Journals (Sweden)

R. Cao

2016-06-01

Full Text Available The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

Science.gov (United States)

Pecevski, Dejan; Maass, Wolfgang

2016-01-01

Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

Probabilistic Inference of Biological Networks via Data Integration

Directory of Open Access Journals (Sweden)

Mark F. Rogers

2015-01-01

Full Text Available There is significant interest in inferring the structure of subcellular networks of interaction. Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links. Many types of data are relevant to inferring functional links between genes, motivating the use of data integration. We use pairwise kernels to predict novel links, along with multiple kernel learning to integrate distinct sources of data into a decision function. We evaluate various pairwise kernels to establish which are most informative and compare individual kernel accuracies with accuracies for weighted combinations. By associating a probability measure with classifier predictions, we enable cautious classification, which can increase accuracy by restricting predictions to high-confidence instances, and data cleaning that can mitigate the influence of mislabeled training instances. Although one pairwise kernel (the tensor product pairwise kernel appears to work best, different kernels may contribute complimentary information about interactions: experiments in S. cerevisiae (yeast reveal that a weighted combination of pairwise kernels applied to different types of data yields the highest predictive accuracy. Combined with cautious classification and data cleaning, we can achieve predictive accuracies of up to 99.6%.

Subjective randomness as statistical inference.

Science.gov (United States)

Griffiths, Thomas L; Daniels, Dylan; Austerweil, Joseph L; Tenenbaum, Joshua B

2018-06-01

Some events seem more random than others. For example, when tossing a coin, a sequence of eight heads in a row does not seem very random. Where do these intuitions about randomness come from? We argue that subjective randomness can be understood as the result of a statistical inference assessing the evidence that an event provides for having been produced by a random generating process. We show how this account provides a link to previous work relating randomness to algorithmic complexity, in which random events are those that cannot be described by short computer programs. Algorithmic complexity is both incomputable and too general to capture the regularities that people can recognize, but viewing randomness as statistical inference provides two paths to addressing these problems: considering regularities generated by simpler computing machines, and restricting the set of probability distributions that characterize regularity. Building on previous work exploring these different routes to a more restricted notion of randomness, we define strong quantitative models of human randomness judgments that apply not just to binary sequences - which have been the focus of much of the previous work on subjective randomness - but also to binary matrices and spatial clustering. Copyright © 2018 Elsevier Inc. All rights reserved.

Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model.

Science.gov (United States)

Nené, Nuno R; Dunham, Alistair S; Illingworth, Christopher J R

2018-05-01

A common challenge arising from the observation of an evolutionary system over time is to infer the magnitude of selection acting upon a specific genetic variant, or variants, within the population. The inference of selection may be confounded by the effects of genetic drift in a system, leading to the development of inference procedures to account for these effects. However, recent work has suggested that deterministic models of evolution may be effective in capturing the effects of selection even under complex models of demography, suggesting the more general application of deterministic approaches to inference. Responding to this literature, we here note a case in which a deterministic model of evolution may give highly misleading inferences, resulting from the nondeterministic properties of mutation in a finite population. We propose an alternative approach that acts to correct for this error, and which we denote the delay-deterministic model. Applying our model to a simple evolutionary system, we demonstrate its performance in quantifying the extent of selection acting within that system. We further consider the application of our model to sequence data from an evolutionary experiment. We outline scenarios in which our model may produce improved results for the inference of selection, noting that such situations can be easily identified via the use of a regular deterministic model. Copyright © 2018 Nené et al.

Exploratory shaft facility preliminary designs - Permian Basin

International Nuclear Information System (INIS)

1983-09-01

The purpose of the Preliminary Design Report, Permian Basin, is to provide a description of the preliminary design for an Exploratory Shaft Facility in the Permian Basin, Texas. This issue of the report describes the preliminary design for constructing the exploratory shaft using the Large Hole Drilling method of construction and outlines the preliminary design and estimates of probable construction cost. The Preliminary Design Report is prepared to complement and summarize other documents that comprise the design at the preliminary stage of completion, December 1982. Other design documents include drawings, cost estimates and schedules. The preliminary design drawing package, which includes the construction schedule drawing, depicts the descriptions in this report. For reference, a list of the drawing titles and corresponding numbers are included in the Appendix. The report is divided into three principal sections: Design Basis, Facility Description, and Construction Cost Estimate. 30 references, 13 tables

Cortical information flow during inferences of agency

NARCIS (Netherlands)

Dogge, Myrthel; Hofman, Dennis; Boersma, Maria; Dijkerman, H Chris; Aarts, Henk

2014-01-01

Building on the recent finding that agency experiences do not merely rely on sensorimotor information but also on cognitive cues, this exploratory study uses electroencephalographic recordings to examine functional connectivity during agency inference processing in a setting where action and outcome

Facility Activity Inference Using Radiation Networks

Energy Technology Data Exchange (ETDEWEB)

Rao, Nageswara S. [ORNL; Ramirez Aviles, Camila A. [ORNL

2017-11-01

We consider the problem of inferring the operational status of a reactor facility using measurements from a radiation sensor network deployed around the facility’s ventilation off-gas stack. The intensity of stack emissions decays with distance, and the sensor counts or measurements are inherently random with parameters determined by the intensity at the sensor’s location. We utilize the measurements to estimate the intensity at the stack, and use it in a one-sided Sequential Probability Ratio Test (SPRT) to infer on/off status of the reactor. We demonstrate the superior performance of this method over conventional majority fusers and individual sensors using (i) test measurements from a network of 21 NaI detectors, and (ii) effluence measurements collected at the stack of a reactor facility. We also analytically establish the superior detection performance of the network over individual sensors with fixed and adaptive thresholds by utilizing the Poisson distribution of the counts. We quantify the performance improvements of the network detection over individual sensors using the packing number of the intensity space.

Inferring climate sensitivity from volcanic events

Energy Technology Data Exchange (ETDEWEB)

Boer, G.J. [Environment Canada, University of Victoria, Canadian Centre for Climate Modelling and Analysis, Victoria, BC (Canada); Stowasser, M.; Hamilton, K. [University of Hawaii, International Pacific Research Centre, Honolulu, HI (United States)

2007-04-15

The possibility of estimating the equilibrium climate sensitivity of the earth-system from observations following explosive volcanic eruptions is assessed in the context of a perfect model study. Two modern climate models (the CCCma CGCM3 and the NCAR CCSM2) with different equilibrium climate sensitivities are employed in the investigation. The models are perturbed with the same transient volcano-like forcing and the responses analysed to infer climate sensitivities. For volcano-like forcing the global mean surface temperature responses of the two models are very similar, despite their differing equilibrium climate sensitivities, indicating that climate sensitivity cannot be inferred from the temperature record alone even if the forcing is known. Equilibrium climate sensitivities can be reasonably determined only if both the forcing and the change in heat storage in the system are known very accurately. The geographic patterns of clear-sky atmosphere/surface and cloud feedbacks are similar for both the transient volcano-like and near-equilibrium constant forcing simulations showing that, to a considerable extent, the same feedback processes are invoked, and determine the climate sensitivity, in both cases. (orig.)

Visual Aids Improve Diagnostic Inferences and Metacognitive Judgment Calibration

Directory of Open Access Journals (Sweden)

Rocio eGarcia-Retamero

2015-07-01

Full Text Available Visual aids can improve comprehension of risks associated with medical treatments, screenings, and lifestyles. Do visual aids also help decision makers accurately assess their risk comprehension? That is, do visual aids help them become well calibrated? To address these questions, we investigated the benefits of visual aids displaying numerical information and measured accuracy of self-assessment of diagnostic inferences (i.e., metacognitive judgment calibration controlling for individual differences in numeracy. Participants included 108 patients who made diagnostic inferences about three medical tests on the basis of information about the sensitivity and false-positive rate of the tests and disease prevalence. Half of the patients received the information in numbers without a visual aid, while the other half received numbers along with a grid representing the numerical information. In the numerical condition, many patients --especially those with low numeracy-- misinterpreted the predictive value of the tests and profoundly overestimated the accuracy of their inferences. Metacognitive judgment calibration mediated the relationship between numeracy and accuracy of diagnostic inferences. In contrast, in the visual aid condition, patients at all levels of numeracy showed high-levels of inferential accuracy and metacognitive judgment calibration. Results indicate that accurate metacognitive assessment may explain the beneficial effects of visual aids and numeracy --a result that accords with theory suggesting that metacognition is an essential part of risk literacy. We conclude that well-designed risk communications can inform patients about health-relevant numerical information while helping them assess the quality of their own risk comprehension.

Inferring time derivatives including cell growth rates using Gaussian processes

Science.gov (United States)

Swain, Peter S.; Stevenson, Keiran; Leary, Allen; Montano-Gutierrez, Luis F.; Clark, Ivan B. N.; Vogel, Jackie; Pilizota, Teuta

2016-12-01

Often the time derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population's growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time derivatives as a function of time from time-series data. Our approach is based on Gaussian processes and applies to a wide range of data. In tests, the method is at least as accurate as others, but has several advantages: it estimates errors both in the inference and in any summary statistics, such as lag times, and allows interpolation with the corresponding error estimation. As illustrations, we infer growth rates of microbial cells, the rate of assembly of an amyloid fibril and both the speed and acceleration of two separating spindle pole bodies. Our algorithm should thus be broadly applicable.

Cultural effects on the association between election outcomes and face-based trait inferences.

Directory of Open Access Journals (Sweden)

Chujun Lin

Full Text Available How competent a politician looks, as assessed in the laboratory, is correlated with whether the politician wins in real elections. This finding has led many to investigate whether the association between candidate appearances and election outcomes transcends cultures. However, these studies have largely focused on European countries and Caucasian candidates. To the best of our knowledge, there are only four cross-cultural studies that have directly investigated how face-based trait inferences correlate with election outcomes across Caucasian and Asian cultures. These prior studies have provided some initial evidence regarding cultural differences, but methodological problems and inconsistent findings have complicated our understanding of how culture mediates the effects of candidate appearances on election outcomes. Additionally, these four past studies have focused on positive traits, with a relative neglect of negative traits, resulting in an incomplete picture of how culture may impact a broader range of trait inferences. To study Caucasian-Asian cultural effects with a more balanced experimental design, and to explore a more complete profile of traits, here we compared how Caucasian and Korean participants' inferences of positive and negative traits correlated with U.S. and Korean election outcomes. Contrary to previous reports, we found that inferences of competence (made by participants from both cultures correlated with both U.S. and Korean election outcomes. Inferences of open-mindedness and threat, two traits neglected in previous cross-cultural studies, were correlated with Korean but not U.S. election outcomes. This differential effect was found in trait judgments made by both Caucasian and Korean participants. Interestingly, the faster the participants made face-based trait inferences, the more strongly those inferences were correlated with real election outcomes. These findings provide new insights into cultural effects and the

Benchmarking Relatedness Inference Methods with Genome-Wide Data from Thousands of Relatives.

Science.gov (United States)

Ramstetter, Monica D; Dyer, Thomas D; Lehman, Donna M; Curran, Joanne E; Duggirala, Ravindranath; Blangero, John; Mezey, Jason G; Williams, Amy L

2017-09-01

Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these approaches in real data has been lacking. Here, we report an assessment of 12 state-of-the-art pairwise relatedness inference methods using a data set with 2485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (92-99%) when detecting first- and second-degree relationships, but their accuracy dwindles to 76% of relative pairs. Overall, the most accurate methods are Estimation of Recent Shared Ancestry (ERSA) and approaches that compute total IBD sharing using the output from GERMLINE and Refined IBD to infer relatedness. Combining information from the most accurate methods provides little accuracy improvement, indicating that novel approaches, such as new methods that leverage relatedness signals from multiple samples, are needed to achieve a sizeable jump in performance. Copyright © 2017 Ramstetter et al.

Experimental and petrological constraints on local-scale interaction of biotite-amphibole gneiss with H2O-CO2-(K, NaCl fluids at middle-crustal conditions: Example from the Limpopo Complex, South Africa

Directory of Open Access Journals (Sweden)

Oleg G. Safonov

2012-11-01

Full Text Available Reaction textures and fluid inclusions in the ∼2.0 Ga pyroxene-bearing dehydration zones within the Sand River biotite-hornblende orthogneisses (Central Zone of the Limpopo Complex suggest that the formation of these zones is a result of close interplay between dehydration process along ductile shear zones triggered by H2O-CO2-salt fluids at 750–800 °C and 5.5–6.2 kbar, partial melting, and later exsolution of residual brine and H2O-CO2 fluids during melt crystallization at 650–700 °C. These processes caused local variations of water and alkali activity in the fluids, resulting in various mineral assemblages within the dehydration zone. The petrological observations are substantiated by experiments on the interaction of the Sand River gneiss with the H2O-CO2-(K, NaCl fluids at 750 and 800 °C and 5.5 kbar. It follows that the interaction of biotite-amphibole gneiss with H2O-CO2-(K, NaCl fluids is accompanied by partial melting at 750–800 °C. Orthopyroxene-bearing assemblages are characteristic for temperature 800 °C and are stable in equilibrium with fluids with low salt concentrations, while salt-rich fluids produce clinopyroxene-bearing assemblages. These observations are in good agreement with the petrological data on the dehydration zones within the Sand River orthogneisses.

Discontinuity of maximum entropy inference and quantum phase transitions

International Nuclear Information System (INIS)

Chen, Jianxin; Ji, Zhengfeng; Yu, Nengkun; Zeng, Bei; Li, Chi-Kwong; Poon, Yiu-Tung; Shen, Yi; Zhou, Duanlu

2015-01-01

In this paper, we discuss the connection between two genuinely quantum phenomena—the discontinuity of quantum maximum entropy inference and quantum phase transitions at zero temperature. It is shown that the discontinuity of the maximum entropy inference of local observable measurements signals the non-local type of transitions, where local density matrices of the ground state change smoothly at the transition point. We then propose to use the quantum conditional mutual information of the ground state as an indicator to detect the discontinuity and the non-local type of quantum phase transitions in the thermodynamic limit. (paper)

Input data for inferring species distributions in Kyphosidae world-wide

Directory of Open Access Journals (Sweden)

Steen Wilhelm Knudsen

2016-09-01

Full Text Available Input data files for inferring the relationship among the family Kyphosidae, as presented in (Knudsen and Clements, 2016 [1], is here provided together with resulting topologies, to allow the reader to explore the topologies in detail. The input data files comprise seven nexus-files with sequence alignments of mtDNA and nDNA markers for performing Bayesian analysis. A matrix of recoded character states inferred from the morphology examined in museum specimens representing Dichistiidae, Girellidae, Kyphosidae, Microcanthidae and Scorpididae, is also provided, and can be used for performing a parsimonious analysis to infer the relationship among these perciform families. The nucleotide input data files comprise both multiple and single representatives of the various species to allow for inference of the relationship among the species in Kyphosidae and between the families closely related to Kyphosidae. The ‘.xml’-files with various constrained relationships among the families potentially closely related to Kyphosidae are also provided to allow the reader to rerun and explore the results from the stepping-stone analysis. The resulting topologies are supplied in newick-file formats together with input data files for Bayesian analysis, together with ‘.xml’-files. Re-running the input data files in the appropriate software, will enable the reader to examine log-files and tree-files themselves. Keywords: Sea chub, Drummer, Kyphosus, Scorpis, Girella

Inference Instruction to Support Reading Comprehension for Elementary Students with Learning Disabilities

Science.gov (United States)

Hall, Colby; Barnes, Marcia A.

2017-01-01

Making inferences during reading is a critical standards-based skill and is important for reading comprehension. This article supports the improvement of reading comprehension for students with learning disabilities (LD) in upper elementary grades by reviewing what is currently known about inference instruction for students with LD and providing…

Statistical inference for extended or shortened phase II studies based on Simon's two-stage designs.

Science.gov (United States)

Zhao, Junjun; Yu, Menggang; Feng, Xi-Ping

2015-06-07

Simon's two-stage designs are popular choices for conducting phase II clinical trials, especially in the oncology trials to reduce the number of patients placed on ineffective experimental therapies. Recently Koyama and Chen (2008) discussed how to conduct proper inference for such studies because they found that inference procedures used with Simon's designs almost always ignore the actual sampling plan used. In particular, they proposed an inference method for studies when the actual second stage sample sizes differ from planned ones. We consider an alternative inference method based on likelihood ratio. In particular, we order permissible sample paths under Simon's two-stage designs using their corresponding conditional likelihood. In this way, we can calculate p-values using the common definition: the probability of obtaining a test statistic value at least as extreme as that observed under the null hypothesis. In addition to providing inference for a couple of scenarios where Koyama and Chen's method can be difficult to apply, the resulting estimate based on our method appears to have certain advantage in terms of inference properties in many numerical simulations. It generally led to smaller biases and narrower confidence intervals while maintaining similar coverages. We also illustrated the two methods in a real data setting. Inference procedures used with Simon's designs almost always ignore the actual sampling plan. Reported P-values, point estimates and confidence intervals for the response rate are not usually adjusted for the design's adaptiveness. Proper statistical inference procedures should be used.

An analysis pipeline for the inference of protein-protein interaction networks

Energy Technology Data Exchange (ETDEWEB)

Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason M.; Cannon, William R.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. H.; Pelletier, Dale A.; Schmoyer, Denise A.; Wiley, H. S.

2009-12-01

An analysis pipeline has been created for deployment of a novel algorithm, the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro), for use in the reconstruction of protein-protein interaction networks. We have combined the Software Environment for BIological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data, and the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources, to allow interactions computed by BEPro to be stored, visualized, and further analyzed. Incorporating BEPro into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of mass spectrometry bait-prey experiment data. SEBINI demo site: https://www.emsl.pnl.gov /SEBINI/ Contact: ronald.taylor@pnl.gov. BEPro is available at http://www.pnl.gov/statistics/BEPro3/index.htm. Contact: ds.daly@pnl.gov. CABIN is available at http://www.sysbio.org/dataresources/cabin.stm. Contact: mudita.singhal@pnl.gov.

POPPER, a simple programming language for probabilistic semantic inference in medicine.

Science.gov (United States)

Robson, Barry

2015-01-01

Our previous reports described the use of the Hyperbolic Dirac Net (HDN) as a method for probabilistic inference from medical data, and a proposed probabilistic medical Semantic Web (SW) language Q-UEL to provide that data. Rather like a traditional Bayes Net, that HDN provided estimates of joint and conditional probabilities, and was static, with no need for evolution due to "reasoning". Use of the SW will require, however, (a) at least the semantic triple with more elaborate relations than conditional ones, as seen in use of most verbs and prepositions, and (b) rules for logical, grammatical, and definitional manipulation that can generate changes in the inference net. Here is described the simple POPPER language for medical inference. It can be automatically written by Q-UEL, or by hand. Based on studies with our medical students, it is believed that a tool like this may help in medical education and that a physician unfamiliar with SW science can understand it. It is here used to explore the considerable challenges of assigning probabilities, and not least what the meaning and utility of inference net evolution would be for a physician. Copyright © 2014 Elsevier Ltd. All rights reserved.

Inferring motion and location using WLAN RSSI

NARCIS (Netherlands)

Kavitha Muthukrishnan, K.; van der Zwaag, B.J.; Havinga, Paul J.M.; Fuller, R.; Koutsoukos, X.

2009-01-01

We present novel algorithms to infer movement by making use of inherent fluctuations in the received signal strengths from existing WLAN infrastructure. We evaluate the performance of the presented algorithms based on classification metrics such as recall and precision using annotated traces

Inferring genetic interactions from comparative fitness data.

Science.gov (United States)

Crona, Kristina; Gavryushkin, Alex; Greene, Devin; Beerenwinkel, Niko

2017-12-20

Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax , the fungus Aspergillus niger , and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.