Bayesian phylogeography finds its roots.
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Philippe Lemey
2009-09-01
Full Text Available As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.
DEFF Research Database (Denmark)
Marske, Katharine Ann; Rahbek, Carsten; Nogues, David Bravo
2013-01-01
highlight three areas where integration of phylogeography with ecological and evolutionary approaches can provide new insights into key questions. First, phylogeography can help clarify the roles of isolation, niche conservatism and environmental stability in generating patterns of alpha- and beta...
Directory of Open Access Journals (Sweden)
Daniel Magee
2017-02-01
Full Text Available Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor. Although the latter appears to enhance the biological validity of ancestral state reconstruction, there has yet to be a comparison of phylogenies created by the two methods. In this paper, we compare these two methods, while also using a primitive method without BSSVS, and highlight the differences in phylogenies created by each. We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014-15 flu season in the U.S. We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors, and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors. Furthermore, the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state, which exhibits the most tropical climate during a typical flu season in the U.S. The GLM, however, appears to be highly susceptible to sampling bias compared with the other methods, which casts doubt on whether its reconstructions should be favored over those created by alternate methods. We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models, and believe that the connection between reconstruction method and sampling bias warrants further investigation.
Finding the optimal Bayesian network given a constraint graph
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Jacob M. Schreiber
2017-07-01
Full Text Available Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables. Fortunately, domain knowledge can frequently be exploited to achieve dramatic computational savings, and in many cases domain knowledge can even make structure learning tractable. Several methods have previously been described for representing this type of structural prior knowledge, including global orderings, super-structures, and constraint rules. While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs. We introduce the concept of a “constraint graph” as an intuitive method for incorporating rich prior knowledge into the structure learning task. We describe how this graph can be used to reduce the memory cost and computational time required to find the optimal graph subject to the encoded constraints, beyond merely eliminating invalid graphs. In particular, we show that a constraint graph can break the structure learning task into independent subproblems even in the presence of cyclic prior knowledge. These subproblems are well suited to being solved in parallel on a single machine or distributed across many machines without excessive communication cost.
BAYESIAN DATA AUGMENTATION DOSE FINDING WITH CONTINUAL REASSESSMENT METHOD AND DELAYED TOXICITY
Liu, Suyu; Yin, Guosheng; Yuan, Ying
2014-01-01
A major practical impediment when implementing adaptive dose-finding designs is that the toxicity outcome used by the decision rules may not be observed shortly after the initiation of the treatment. To address this issue, we propose the data augmentation continual re-assessment method (DA-CRM) for dose finding. By naturally treating the unobserved toxicities as missing data, we show that such missing data are nonignorable in the sense that the missingness depends on the unobserved outcomes. The Bayesian data augmentation approach is used to sample both the missing data and model parameters from their posterior full conditional distributions. We evaluate the performance of the DA-CRM through extensive simulation studies, and also compare it with other existing methods. The results show that the proposed design satisfactorily resolves the issues related to late-onset toxicities and possesses desirable operating characteristics: treating patients more safely, and also selecting the maximum tolerated dose with a higher probability. The new DA-CRM is illustrated with two phase I cancer clinical trials. PMID:24707327
Examples of Video to Communicate Scientific Findings to Non-Scientists-Bayesian Ecological Modeling
Moorman, M.; Harned, D. A.; Cuffney, T.; Qian, S.
2011-12-01
The U.S Geological Survey (USGS) National Water-Quality Assessment Program (NAWQA) provides information about (1) water-quality conditions and how those conditions vary locally, regionally, and nationally, (2) water-quality trends, and (3) factors that affect those conditions. As part of the NAWQA Program, the Effects of Urbanization on Stream Ecosystems (EUSE) study examined the vulnerability and resilience of streams to urbanization. Completion of the EUSE study has resulted in over 20 scientific publications. Video podcasts are being used in addition to these publications to communicate the relevance of these scientific findings to more general audiences such as resource managers, educational groups, public officials, and the general public. An example of one of the podcasts is a film about the results of modeling the effects urbanization on stream ecology. The film describes some of the results of the EUSE ecological modeling effort and the advantages of the Bayesian and multi-level statistical modeling approaches, while relating the science to fly fishing. The complex scientific discussion combined with the lighter, more popular activity of fly fishing leads to an entertaining forum while educating viewers about a complex topic. This approach is intended to represent the scientists as interesting people with diverse interests. Video can be an effective scientific communication tool for presenting scientific findings to a broad audience. The film is available for access from the EUSE website (http://water.usgs.gov/nawqa/urban/html/podcasts.html). Additional films are planned to be released in 2012 on other USGS project results and programs.
Phylogeography of the Central American lancehead Bothrops asper (SERPENTES: VIPERIDAE)
Parkinson, Christopher L.; Daza, Juan M.; Wüster, Wolfgang
2017-01-01
The uplift and final connection of the Central American land bridge is considered the major event that allowed biotic exchange between vertebrate lineages of northern and southern origin in the New World. However, given the complex tectonics that shaped Middle America, there is still substantial controversy over details of this geographical reconnection, and its role in determining biogeographic patterns in the region. Here, we examine the phylogeography of Bothrops asper, a widely distributed pitviper in Middle America and northwestern South America, in an attempt to evaluate how the final Isthmian uplift and other biogeographical boundaries in the region influenced genealogical lineage divergence in this species. We examined sequence data from two mitochondrial genes (MT-CYB and MT-ND4) from 111 specimens of B. asper, representing 70 localities throughout the species’ distribution. We reconstructed phylogeographic patterns using maximum likelihood and Bayesian methods and estimated divergence time using the Bayesian relaxed clock method. Within the nominal species, an early split led to two divergent lineages of B. asper: one includes five phylogroups distributed in Caribbean Middle America and southwestern Ecuador, and the other comprises five other groups scattered in the Pacific slope of Isthmian Central America and northwestern South America. Our results provide evidence of a complex transition that involves at least two dispersal events into Middle America during the final closure of the Isthmus. PMID:29176806
Phylogeography of Rattus norvegicus in the South Atlantic Ocean
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Melanie Hingston
2016-12-01
Full Text Available Norway rats are a globally distributed invasive species, which have colonized many islands around the world, including in the South Atlantic Ocean. We investigated the phylogeography of Norway rats across the South Atlantic Ocean and bordering continental countries. We identified haplotypes from 517 bp of the hypervariable region I of the mitochondrial D-loop and constructed a Bayesian consensus tree and median-joining network incorporating all other publicly available haplotypes via an alignment of 364 bp. Three Norway rat haplotypes are present across the islands of the South Atlantic Ocean, including multiple haplotypes separated by geographic barriers within island groups. All three haplotypes have been previously recorded from European countries. Our results support the hypothesis of rapid Norway rat colonization of South Atlantic Ocean islands by sea-faring European nations from multiple European ports of origin. This seems to have been the predominant pathway for repeated Norway rat invasions of islands, even within the same archipelago, rather than within-island dispersal across geographic barriers.
The origin and phylogeography of dog rabies virus
Bourhy, Hervé; Reynes, Jean-Marc; Dunham, Eleca J.; Dacheux, Laurent; Larrous, Florence; Huong, Vu Thi Que; Xu, Gelin; Yan, Jiaxin; Miranda, Mary Elizabeth G.; Holmes, Edward C.
2012-01-01
Rabies is a progressively fatal and incurable viral encephalitis caused by a lyssavirus infection. Almost all of the 55 000 annual rabies deaths in humans result from infection with dog rabies viruses (RABV). Despite the importance of rabies for human health, little is known about the spread of RABV in dog populations, and patterns of biodiversity have only been studied in limited geographical space. To address these questions on a global scale, we sequenced 62 new isolates and performed an extensive comparative analysis of RABV gene sequence data, representing 192 isolates sampled from 55 countries. From this, we identified six clades of RABV in non-flying mammals, each of which has a distinct geographical distribution, most likely reflecting major physical barriers to gene flow. Indeed, a detailed analysis of phylogeographic structure revealed only limited viral movement among geographical localities. Using Bayesian coalescent methods we also reveal that the sampled lineages of canid RABV derive from a common ancestor that originated within the past 1500 years. Additionally, we found no evidence for either positive selection or widespread population bottlenecks during the global expansion of canid RABV. Overall, our study reveals that the stochastic processes of genetic drift and population subdivision are the most important factors shaping the global phylogeography of canid RABV. PMID:18931062
Wang, Xiu-Ling; Sun, Jian-Yun; Xue, Yan; Zhang, Peng; Zhou, Hui; Qu, Liang-Hu
2012-01-01
Background The Siberian salamander (Ranodon sibiricus), distributed in geographically isolated areas of Central Asia, is an ideal alpine species for studies of conservation and phylogeography. However, there are few data regarding the genetic diversity in R. sibiricus populations. Methodology/Principal Findings We used two genetic markers (mtDNA and microsatellites) to survey all six populations of R. sibiricus in China. Both of the markers revealed extreme genetic uniformity among these populations. There were only three haplotypes in the mtDNA, and the overall nucleotide diversity in the mtDNA was 0.00064, ranging from 0.00000 to 0.00091 for the six populations. Although we recovered 70 sequences containing microsatellite repeats, there were only two loci that displayed polymorphism. We used the approximate Bayesian computation (ABC) method to study the demographic history of the populations. This analysis suggested that the extant populations diverged from the ancestral population approximately 120 years ago and that the historical population size was much larger than the present population size; i.e., R. sibiricus has experienced dramatic population declines. Conclusion/Significance Our findings suggest that the genetic diversity in the R. sibiricus populations is the lowest among all investigated amphibians. We conclude that the isolation of R. sibiricus populations occurred recently and was a result of recent human activity and/or climatic changes. The Pleistocene glaciation oscillations may have facilitated intraspecies genetic homogeneity rather than enhanced divergence. A low genomic evolutionary rate and elevated inbreeding frequency may have also contributed to the low genetic variation observed in this species. Our findings indicate the urgency of implementing a protection plan for this endangered species. PMID:22428037
Phylogeography by diffusion on a sphere: whole world phylogeography
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Remco Bouckaert
2016-09-01
Full Text Available Background Techniques for reconstructing geographical history along a phylogeny can answer many questions of interest about the geographical origins of species. Bayesian models based on the assumption that taxa move through a diffusion process have found many applications. However, these methods rely on diffusion processes on a plane, and do not take the spherical nature of our planet in account. Performing an analysis that covers the whole world thus does not take in account the distortions caused by projections like the Mercator projection. Results In this paper, we introduce a Bayesian phylogeographical method based on diffusion on a sphere. When the area where taxa are sampled from is small, a sphere can be approximated by a plane and the model results in the same inferences as with models using diffusion on a plane. For taxa sampled from the whole world, we obtain substantial differences. We present an efficient algorithm for performing inference in a Markov Chain Monte Carlo (MCMC algorithm, and show applications to small and large samples areas. We compare results between planar and spherical diffusion in a simulation study and apply the method by inferring the origin of Hepatitis B based on sequences sampled from Eurasia and Africa. Conclusions We describe a framework for performing phylogeographical inference, which is suitable when the distortion introduced by map projections is large, but works well on a smaller scale as well. The framework allows sampling tips from regions, which is useful when the exact sample location is unknown, and placing prior information on locations of clades in the tree. The method is implemented in the GEO_SPHERE package in BEAST 2, which is open source licensed under LGPL and allows joint tree and geography inference under a wide range of models.
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Florentia Kaguelidou
Full Text Available Proton pump inhibitors are frequently administered on clinical symptoms in neonates but benefit remains controversial. Clinical trials validating omeprazole dosage in neonates are limited. The objective of this trial was to determine the minimum effective dose (MED of omeprazole to treat pathological acid reflux in neonates using reflux index as surrogate marker.Double blind dose-finding trial with continual reassessment method of individual dose administration using a Bayesian approach, aiming to select drug dose as close as possible to the predefined target level of efficacy (with a credibility interval of 95%.Neonatal Intensive Care unit of the Robert Debré University Hospital in Paris, France.Neonates with a postmenstrual age ≥ 35 weeks and a pathologic 24-hour intra-esophageal pH monitoring defined by a reflux index ≥ 5% over 24 hours were considered for participation. Recruitment was stratified to 3 groups according to gestational age at birth.Five preselected doses of oral omeprazole from 1 to 3 mg/kg/day.Primary outcome, measured at 35 weeks postmenstrual age or more, was a reflux index <5% during the 24-h pH monitoring registered 72±24 hours after omeprazole initiation.Fifty-four neonates with a reflux index ranging from 5.06 to 27.7% were included. Median age was 37.5 days and median postmenstrual age was 36 weeks. In neonates born at less than 32 weeks of GA (n = 30, the MED was 2.5mg/kg/day with an estimated mean posterior probability of success of 97.7% (95% credibility interval: 90.3-99.7%. The MED was 1mg/kg/day for neonates born at more than 32 GA (n = 24.Omeprazole is extensively prescribed on clinical symptoms but efficacy is not demonstrated while safety concerns do exist. When treatment is required, the daily dose needs to be validated in preterm and term neonates. Optimal doses of omeprazole to increase gastric pH and decrease reflux index below 5% over 24 hours, determined using an adaptive Bayesian design differ
Kaguelidou, Florentia; Alberti, Corinne; Biran, Valerie; Bourdon, Olivier; Farnoux, Caroline; Zohar, Sarah; Jacqz-Aigrain, Evelyne
2016-01-01
Proton pump inhibitors are frequently administered on clinical symptoms in neonates but benefit remains controversial. Clinical trials validating omeprazole dosage in neonates are limited. The objective of this trial was to determine the minimum effective dose (MED) of omeprazole to treat pathological acid reflux in neonates using reflux index as surrogate marker. Double blind dose-finding trial with continual reassessment method of individual dose administration using a Bayesian approach, aiming to select drug dose as close as possible to the predefined target level of efficacy (with a credibility interval of 95%). Neonatal Intensive Care unit of the Robert Debré University Hospital in Paris, France. Neonates with a postmenstrual age ≥ 35 weeks and a pathologic 24-hour intra-esophageal pH monitoring defined by a reflux index ≥ 5% over 24 hours were considered for participation. Recruitment was stratified to 3 groups according to gestational age at birth. Five preselected doses of oral omeprazole from 1 to 3 mg/kg/day. Primary outcome, measured at 35 weeks postmenstrual age or more, was a reflux index reflux index ranging from 5.06 to 27.7% were included. Median age was 37.5 days and median postmenstrual age was 36 weeks. In neonates born at less than 32 weeks of GA (n = 30), the MED was 2.5mg/kg/day with an estimated mean posterior probability of success of 97.7% (95% credibility interval: 90.3-99.7%). The MED was 1mg/kg/day for neonates born at more than 32 GA (n = 24). Omeprazole is extensively prescribed on clinical symptoms but efficacy is not demonstrated while safety concerns do exist. When treatment is required, the daily dose needs to be validated in preterm and term neonates. Optimal doses of omeprazole to increase gastric pH and decrease reflux index below 5% over 24 hours, determined using an adaptive Bayesian design differ among neonates. Both gestational and postnatal ages account for these differences but their differential impact on omeprazole
Lesaffre, Emmanuel
2012-01-01
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd
ZHOU, Lin
1996-01-01
In this paper I consider social choices under uncertainty. I prove that any social choice rule that satisfies independence of irrelevant alternatives, translation invariance, and weak anonymity is consistent with ex post Bayesian utilitarianism
Salvato, M.; Buchner, J.; Budavári, T.; Dwelly, T.; Merloni, A.; Brusa, M.; Rau, A.; Fotopoulou, S.; Nandra, K.
2018-02-01
We release the AllWISE counterparts and Gaia matches to 106 573 and 17 665 X-ray sources detected in the ROSAT 2RXS and XMMSL2 surveys with |b| > 15°. These are the brightest X-ray sources in the sky, but their position uncertainties and the sparse multi-wavelength coverage until now rendered the identification of their counterparts a demanding task with uncertain results. New all-sky multi-wavelength surveys of sufficient depth, like AllWISE and Gaia, and a new Bayesian statistics based algorithm, NWAY, allow us, for the first time, to provide reliable counterpart associations. NWAY extends previous distance and sky density based association methods and, using one or more priors (e.g. colours, magnitudes), weights the probability that sources from two or more catalogues are simultaneously associated on the basis of their observable characteristics. Here, counterparts have been determined using a Wide-field Infrared Survey Explorer (WISE) colour-magnitude prior. A reference sample of 4524 XMM/Chandra and Swift X-ray sources demonstrates a reliability of ∼94.7 per cent (2RXS) and 97.4 per cent (XMMSL2). Combining our results with Chandra-COSMOS data, we propose a new separation between stars and AGN in the X-ray/WISE flux-magnitude plane, valid over six orders of magnitude. We also release the NWAY code and its user manual. NWAY was extensively tested with XMM-COSMOS data. Using two different sets of priors, we find an agreement of 96 per cent and 99 per cent with published Likelihood Ratio methods. Our results were achieved faster and without any follow-up visual inspection. With the advent of deep and wide area surveys in X-rays (e.g. SRG/eROSITA, Athena/WFI) and radio (ASKAP/EMU, LOFAR, APERTIF, etc.) NWAY will provide a powerful and reliable counterpart identification tool.
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.
Burns, Kevin J; Barhoum, Dino N
2006-01-01
The phylogeography of a variety of species has been studied within the California Floristic Province; however, few studies have examined genetic variation in bird species across the entire region. This study uses mitochondrial DNA data to investigate the phylogeography of the wrentit (Chamaea fasciata), a sedentary bird native to scrub and chaparral habitats of this region. Analysis of molecular variance shows geographic structure, and maximum likelihood, Bayesian, and parsimony analyses consistently identify six main clades that are each restricted geographically. Nested clade phylogeographic analyses infer an overall range expansion for the entire cladogram, and a range expansion is also inferred from the mismatch distribution. Thus, our results suggest that the wrentit was isolated into southern refugia during the Pleistocene and has undergone a recent range expansion. Southern refugia and a range expansion were also identified in a previous study of the California thrasher (Toxostoma redivivum). The wrentit did not show marked divergence between northern and southern California defined by the Transverse Ranges, a pattern seen in a variety of other taxa within this region, including some birds.
Bekker, Eugeniya I; Karabanov, Dmitry P; Galimov, Yan R; Haag, Christoph R; Neretina, Tatiana V; Kotov, Alexey A
2018-01-01
Species with a large geographic distributions present a challenge for phylogeographic studies due to logistic difficulties of obtaining adequate sampling. For instance, in most species with a Holarctic distribution, the majority of studies has concentrated on the European or North American part of the distribution, with the Eastern Palearctic region being notably understudied. Here, we study the phylogeography of the freshwater cladoceran Daphnia magna Straus, 1820 (Crustacea: Cladocera), based on partial mitochondrial COI sequences and using specimens from populations spread longitudinally from westernmost Europe to easternmost Asia, with many samples from previously strongly understudied regions in Siberia and Eastern Asia. The results confirm the previously suspected deep split between Eastern and Western mitochondrial haplotype super-clades. We find a narrow contact zone between these two super-clades in the eastern part of Western Siberia, with proven co-occurrence in a single lake in the Novosibirsk region. However, at present there is no evidence suggesting that the two mitochondrial super-clades represent cryptic species. Rather, they may be explained by secondary contact after expansion from different refugia. Interestingly, Central Siberia has previously been found to be an important contact zone also in other cladoceran species, and may thus be a crucial area for understanding the Eurasian phylogeography of freshwater invertebrates. Together, our study provides an unprecedented complete, while still not global, picture of the phylogeography of this important model species.
Comparative phylogeography: concepts, methods and general patterns in neotropical birds
International Nuclear Information System (INIS)
Arbelaez Cortes, Enrique
2012-01-01
Understanding the patterns and processes involved in intraspecific lineages diversification in time and space is the aim of phylogeography. The comparison of those phylogeographic patterns among co-distributed species shows insights of a community history. Here I review the concepts and methodologies of comparative phylogeography, an active research field that has heterogeneous analytical methods. In order to present a framework for phylogeography in the neotropics, I comment the general phylogeographic patterns of the birds from this region. this review is based on more than 100 studies conducted during the last 25 years and indicate that despite different co-distributed species seem to share some points in their phylogeographic pattern they have idiosyncratic aspects, indicating an unique history for each one.
Directory of Open Access Journals (Sweden)
de Thoisy Benoit
2010-09-01
Full Text Available Abstract Background Understanding the forces that shaped Neotropical diversity is central issue to explain tropical biodiversity and inform conservation action; yet few studies have examined large, widespread species. Lowland tapir (Tapirus terrrestris, Perissodactyla, Tapiridae is the largest Neotropical herbivore whose ancestors arrived in South America during the Great American Biotic Interchange. A Pleistocene diversification is inferred for the genus Tapirus from the fossil record, but only two species survived the Pleistocene megafauna extinction. Here, we investigate the history of lowland tapir as revealed by variation at the mitochondrial gene Cytochrome b, compare it to the fossil data, and explore mechanisms that could have shaped the observed structure of current populations. Results Separate methodological approaches found mutually exclusive divergence times for lowland tapir, either in the late or in the early Pleistocene, although a late Pleistocene divergence is more in tune with the fossil record. Bayesian analysis favored mountain tapir (T. pinchaque paraphyly in relation to lowland tapir over reciprocal monophyly, corroborating the inferences from the fossil data these species are sister taxa. A coalescent-based analysis rejected a null hypothesis of allopatric divergence, suggesting a complex history. Based on the geographic distribution of haplotypes we propose (i a central role for western Amazonia in tapir diversification, with a key role of the ecological gradient along the transition between Andean subcloud forests and Amazon lowland forest, and (ii that the Amazon river acted as an barrier to gene flow. Finally, the branching patterns and estimates based on nucleotide diversity indicate a population expansion after the Last Glacial Maximum. Conclusions This study is the first examining lowland tapir phylogeography. Climatic events at the end of the Pleistocene, parapatric speciation, divergence along the Andean foothill
de Thoisy, Benoit; da Silva, Anders Gonçalves; Ruiz-García, Manuel; Tapia, Andrés; Ramirez, Oswaldo; Arana, Margarita; Quse, Viviana; Paz-y-Miño, César; Tobler, Mathias; Pedraza, Carlos; Lavergne, Anne
2010-09-14
Understanding the forces that shaped Neotropical diversity is central issue to explain tropical biodiversity and inform conservation action; yet few studies have examined large, widespread species. Lowland tapir (Tapirus terrrestris, Perissodactyla, Tapiridae) is the largest Neotropical herbivore whose ancestors arrived in South America during the Great American Biotic Interchange. A Pleistocene diversification is inferred for the genus Tapirus from the fossil record, but only two species survived the Pleistocene megafauna extinction. Here, we investigate the history of lowland tapir as revealed by variation at the mitochondrial gene Cytochrome b, compare it to the fossil data, and explore mechanisms that could have shaped the observed structure of current populations. Separate methodological approaches found mutually exclusive divergence times for lowland tapir, either in the late or in the early Pleistocene, although a late Pleistocene divergence is more in tune with the fossil record. Bayesian analysis favored mountain tapir (T. pinchaque) paraphyly in relation to lowland tapir over reciprocal monophyly, corroborating the inferences from the fossil data these species are sister taxa. A coalescent-based analysis rejected a null hypothesis of allopatric divergence, suggesting a complex history. Based on the geographic distribution of haplotypes we propose (i) a central role for western Amazonia in tapir diversification, with a key role of the ecological gradient along the transition between Andean subcloud forests and Amazon lowland forest, and (ii) that the Amazon river acted as an barrier to gene flow. Finally, the branching patterns and estimates based on nucleotide diversity indicate a population expansion after the Last Glacial Maximum. This study is the first examining lowland tapir phylogeography. Climatic events at the end of the Pleistocene, parapatric speciation, divergence along the Andean foothill, and role of the Amazon river, have similarly shaped
Bessiere, Pierre; Ahuactzin, Juan Manuel; Mekhnacha, Kamel
2013-01-01
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean
Celtic fringe of Britain: insights from small mammal phylogeography
Czech Academy of Sciences Publication Activity Database
Searle, J. B.; Kotlík, Petr; Rambau, R.V.; Marková, Silvia; Herman, J.S.; McDevitt, A.D.
2009-01-01
Roč. 276, č. 1677 (2009), s. 4287-4294 ISSN 0962-8452 R&D Projects: GA AV ČR IAA600450701; GA AV ČR IAA600450901 Institutional research plan: CEZ:AV0Z50450515 Keywords : Phylogeography * Myodes glareolus * Celtic fringle Subject RIV: EH - Ecology, Behaviour Impact factor: 4.857, year: 2009
Parallel Mitogenome Sequencing Alleviates Random Rooting Effect in Phylogeography.
Hirase, Shotaro; Takeshima, Hirohiko; Nishida, Mutsumi; Iwasaki, Wataru
2016-04-28
Reliably rooted phylogenetic trees play irreplaceable roles in clarifying diversification in the patterns of species and populations. However, such trees are often unavailable in phylogeographic studies, particularly when the focus is on rapidly expanded populations that exhibit star-like trees. A fundamental bottleneck is known as the random rooting effect, where a distant outgroup tends to root an unrooted tree "randomly." We investigated whether parallel mitochondrial genome (mitogenome) sequencing alleviates this effect in phylogeography using a case study on the Sea of Japan lineage of the intertidal goby Chaenogobius annularis Eighty-three C. annularis individuals were collected and their mitogenomes were determined by high-throughput and low-cost parallel sequencing. Phylogenetic analysis of these mitogenome sequences was conducted to root the Sea of Japan lineage, which has a star-like phylogeny and had not been reliably rooted. The topologies of the bootstrap trees were investigated to determine whether the use of mitogenomes alleviated the random rooting effect. The mitogenome data successfully rooted the Sea of Japan lineage by alleviating the effect, which hindered phylogenetic analysis that used specific gene sequences. The reliable rooting of the lineage led to the discovery of a novel, northern lineage that expanded during an interglacial period with high bootstrap support. Furthermore, the finding of this lineage suggested the existence of additional glacial refugia and provided a new recent calibration point that revised the divergence time estimation between the Sea of Japan and Pacific Ocean lineages. This study illustrates the effectiveness of parallel mitogenome sequencing for solving the random rooting problem in phylogeographic studies. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Strategies for improving approximate Bayesian computation tests for synchronous diversification.
Overcast, Isaac; Bagley, Justin C; Hickerson, Michael J
2017-08-24
Estimating the variability in isolation times across co-distributed taxon pairs that may have experienced the same allopatric isolating mechanism is a core goal of comparative phylogeography. The use of hierarchical Approximate Bayesian Computation (ABC) and coalescent models to infer temporal dynamics of lineage co-diversification has been a contentious topic in recent years. Key issues that remain unresolved include the choice of an appropriate prior on the number of co-divergence events (Ψ), as well as the optimal strategies for data summarization. Through simulation-based cross validation we explore the impact of the strategy for sorting summary statistics and the choice of prior on Ψ on the estimation of co-divergence variability. We also introduce a new setting (β) that can potentially improve estimation of Ψ by enforcing a minimal temporal difference between pulses of co-divergence. We apply this new method to three empirical datasets: one dataset each of co-distributed taxon pairs of Panamanian frogs and freshwater fishes, and a large set of Neotropical butterfly sister-taxon pairs. We demonstrate that the choice of prior on Ψ has little impact on inference, but that sorting summary statistics yields substantially more reliable estimates of co-divergence variability despite violations of assumptions about exchangeability. We find the implementation of β improves estimation of Ψ, with improvement being most dramatic given larger numbers of taxon pairs. We find equivocal support for synchronous co-divergence for both of the Panamanian groups, but we find considerable support for asynchronous divergence among the Neotropical butterflies. Our simulation experiments demonstrate that using sorted summary statistics results in improved estimates of the variability in divergence times, whereas the choice of hyperprior on Ψ has negligible effect. Additionally, we demonstrate that estimating the number of pulses of co-divergence across co-distributed taxon
DEFF Research Database (Denmark)
Jensen, Finn Verner; Nielsen, Thomas Dyhre
2016-01-01
Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes...
Directory of Open Access Journals (Sweden)
Sozos Michaelides
Full Text Available Populations at range limits are often characterized by lower genetic diversity, increased genetic isolation and differentiation relative to populations at the core of geographical ranges. Furthermore, it is increasingly recognized that populations situated at range limits might be the result of human introductions rather than natural dispersal. It is therefore important to document the origin and genetic diversity of marginal populations to establish conservation priorities. In this study, we investigate the phylogeography and genetic structure of peripheral populations of the common European wall lizard, Podarcis muralis, on Jersey (Channel Islands, UK and in the Chausey archipelago. We sequenced a fragment of the mitochondrial cytochrome b gene in 200 individuals of P. muralis to infer the phylogeography of the island populations using Bayesian approaches. We also genotyped 484 individuals from 21 populations at 10 polymorphic microsatellite loci to evaluate the genetic structure and diversity of island and mainland (Western France populations. We detected four unique haplotypes in the island populations that formed a sub-clade within the Western France clade. There was a significant reduction in genetic diversity (HO, HE and AR of the island populations in relation to the mainland. The small fragmented island populations at the northern range margin of the common wall lizard distribution are most likely native, with genetic differentiation reflecting isolation following sea level increase approximately 7000 BP. Genetic diversity is lower on islands than in marginal populations on the mainland, potentially as a result of early founder effects or long-term isolation. The combination of restriction to specific localities and an inability to expand their range into adjacent suitable locations might make the island populations more vulnerable to extinction.
Introduction to Bayesian statistics
Bolstad, William M
2017-01-01
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...
Bayesian natural language semantics and pragmatics
Zeevat, Henk
2015-01-01
The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice's contributions to pragmatics or in interpretation by abduction.
Bayesian artificial intelligence
Korb, Kevin B
2010-01-01
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente
Bayesian artificial intelligence
Korb, Kevin B
2003-01-01
As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.
Bayesian analysis of CCDM models
Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.
2017-09-01
Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3αH0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.
Bayesian analysis of CCDM models
Energy Technology Data Exchange (ETDEWEB)
Jesus, J.F. [Universidade Estadual Paulista (Unesp), Câmpus Experimental de Itapeva, Rua Geraldo Alckmin 519, Vila N. Sra. de Fátima, Itapeva, SP, 18409-010 Brazil (Brazil); Valentim, R. [Departamento de Física, Instituto de Ciências Ambientais, Químicas e Farmacêuticas—ICAQF, Universidade Federal de São Paulo (UNIFESP), Unidade José Alencar, Rua São Nicolau No. 210, Diadema, SP, 09913-030 Brazil (Brazil); Andrade-Oliveira, F., E-mail: jfjesus@itapeva.unesp.br, E-mail: valentim.rodolfo@unifesp.br, E-mail: felipe.oliveira@port.ac.uk [Institute of Cosmology and Gravitation—University of Portsmouth, Burnaby Road, Portsmouth, PO1 3FX United Kingdom (United Kingdom)
2017-09-01
Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3α H {sub 0} model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.
Yuan, Ying; MacKinnon, David P.
2009-01-01
This article proposes Bayesian analysis of mediation effects. Compared to conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian mediation analysis, inference is straightforward and exact, which makes it appealing for studies with small samples. Third, the Bayesian approach is conceptua...
Marsman, M.; Wagenmakers, E.-J.
2017-01-01
We illustrate the Bayesian approach to data analysis using the newly developed statistical software program JASP. With JASP, researchers are able to take advantage of the benefits that the Bayesian framework has to offer in terms of parameter estimation and hypothesis testing. The Bayesian
Directory of Open Access Journals (Sweden)
Camila Madeira Tavares Lopes
Full Text Available BACKGROUND Didelphis spp. are a South American marsupial species that are among the most ancient hosts for the Trypanosoma spp. OBJECTIVES We characterise a new species (Trypanosoma janseni n. sp. isolated from the spleen and liver tissues of Didelphis aurita in the Atlantic Rainforest of Rio de Janeiro, Brazil. METHODS The parasites were isolated and a growth curve was performed in NNN and Schneider's media containing 10% foetal bovine serum. Parasite morphology was evaluated via light microscopy on Giemsa-stained culture smears, as well as scanning and transmission electron microscopy. Molecular taxonomy was based on a partial region (737-bp of the small subunit (18S ribosomal RNA gene and 708 bp of the nuclear marker, glycosomal glyceraldehyde-3-phosphate dehydrogenase (gGAPDH genes. Maximum likelihood and Bayesian inference methods were used to perform a species coalescent analysis and to generate individual and concatenated gene trees. Divergence times among species that belong to the T. cruzi clade were also inferred. FINDINGS In vitro growth curves demonstrated a very short log phase, achieving a maximum growth rate at day 3 followed by a sharp decline. Only epimastigote forms were observed under light and scanning microscopy. Transmission electron microscopy analysis showed structures typical to Trypanosoma spp., except one structure that presented as single-membraned, usually grouped in stacks of three or four. Phylogeography analyses confirmed the distinct species status of T. janseni n. sp. within the T. cruzi clade. Trypanosoma janseni n. sp. clusters with T. wauwau in a well-supported clade, which is exclusive and monophyletic. The separation of the South American T. wauwau + T. janseni coincides with the separation of the Southern Super Continent. CONCLUSIONS This clade is a sister group of the trypanosomes found in Australian marsupials and its discovery sheds light on the initial diversification process based on what we currently
Phylogeography of Asian wild rice, Oryza rufipogon: a genome-wide view.
Huang, Pu; Molina, Jeanmaire; Flowers, Jonathan M; Rubinstein, Samara; Jackson, Scott A; Purugganan, Michael D; Schaal, Barbara A
2012-09-01
Asian wild rice (Oryza rufipogon) that ranges widely across the eastern and southern part of Asia is recognized as the direct ancestor of cultivated Asian rice (O. sativa). Studies of the geographic structure of O. rufipogon, based on chloroplast and low-copy nuclear markers, reveal a possible phylogeographic signal of subdivision in O. rufipogon. However, this signal of geographic differentiation is not consistently observed among different markers and studies, with often conflicting results. To more precisely characterize the phylogeography of O. rufipogon populations, a genome-wide survey of unlinked markers, intensively sampled from across the entire range of O. rufipogon is critical. In this study, we surveyed sequence variation at 42 genome-wide sequence tagged sites (STS) in 108 O. rufipogon accessions from throughout the native range of the species. Using Bayesian clustering, principal component analysis and amova, we conclude that there are two genetically distinct O. rufipogon groups, Ruf-I and Ruf-II. The two groups exhibit a clinal variation pattern generally from north-east to south-west. Different from many earlier studies, Ruf-I, which is found mainly in China and the Indochinese Peninsula, shows genetic similarity with one major cultivated rice variety, O. satvia indica, whereas Ruf-II, mainly from South Asia and the Indochinese Peninsula, is not found to be closely related to cultivated rice varieties. The other major cultivated rice variety, O. sativa japonica, is not found to be similar to either O. rufipogon groups. Our results support the hypothesis of a single origin of the domesticated O. sativa in China. The possible role of palaeoclimate, introgression and migration-drift balance in creating this clinal variation pattern is also discussed. © 2012 Blackwell Publishing Ltd.
Kinkar, Liina; Laurimäe, Teivi; Acosta-Jamett, Gerardo; Andresiuk, Vanessa; Balkaya, Ibrahim; Casulli, Adriano; Gasser, Robin B; van der Giessen, Joke; González, Luis Miguel; Haag, Karen L; Zait, Houria; Irshadullah, Malik; Jabbar, Abdul; Jenkins, David J; Kia, Eshrat Beigom; Manfredi, Maria Teresa; Mirhendi, Hossein; M'rad, Selim; Rostami-Nejad, Mohammad; Oudni-M'rad, Myriam; Pierangeli, Nora Beatriz; Ponce-Gordo, Francisco; Rehbein, Steffen; Sharbatkhori, Mitra; Simsek, Sami; Soriano, Silvia Viviana; Sprong, Hein; Šnábel, Viliam; Umhang, Gérald; Varcasia, Antonio; Saarma, Urmas
2018-05-19
Echinococcus granulosus sensu stricto (s.s.) is the major cause of human cystic echinococcosis worldwide and is listed among the most severe parasitic diseases of humans. To date, numerous studies have investigated the genetic diversity and population structure of E. granulosus s.s. in various geographic regions. However, there has been no global study. Recently, using mitochondrial DNA, it was shown that E. granulosus s.s. G1 and G3 are distinct genotypes, but a larger dataset is required to confirm the distinction of these genotypes. The objectives of this study were to: (i) investigate the distinction of genotypes G1 and G3 using a large global dataset; and (ii) analyse the genetic diversity and phylogeography of genotype G1 on a global scale using near-complete mitogenome sequences. For this study, 222 globally distributed E. granulosus s.s. samples were used, of which 212 belonged to genotype G1 and 10 to G3. Using a total sequence length of 11,682 bp, we inferred phylogenetic networks for three datasets: E. granulosus s.s. (n = 222), G1 (n = 212) and human G1 samples (n = 41). In addition, the Bayesian phylogenetic and phylogeographic analyses were performed. The latter yielded several strongly supported diffusion routes of genotype G1 originating from Turkey, Tunisia and Argentina. We conclude that: (i) using a considerably larger dataset than employed previously, E. granulosus s.s. G1 and G3 are indeed distinct mitochondrial genotypes; (ii) the genetic diversity of E. granulosus s.s. G1 is high globally, with lower values in South America; and (iii) the complex phylogeographic patterns emerging from the phylogenetic and geographic analyses suggest that the current distribution of genotype G1 has been shaped by intensive animal trade. Copyright © 2018 Australian Society for Parasitology. Published by Elsevier Ltd. All rights reserved.
Pan-African phylogeography of a model organism, the African clawed frog "Xenopus laevis"
Czech Academy of Sciences Publication Activity Database
Furman, B. L. S.; Bewick, A. J.; Harrison, T. L.; Greenbaum, E.; Gvoždík, Václav; Kusamba, C.; Evans, B. J.
2015-01-01
Roč. 24, č. 4 (2015), s. 909-925 ISSN 0962-1083 Institutional support: RVO:68081766 Keywords : gene flow * phylogeography * population genetics * species limits Subject RIV: EG - Zoology Impact factor: 5.947, year: 2015
Waldrop, Ellen; Hobbs, Jean-Paul A.; Randall, John E.; DiBattista, Joseph; Rocha, Luiz A.; Kosaki, Randall K.; Berumen, Michael L.; Bowen, Brian W.
2016-01-01
This study compares the phylogeography, population structure and evolution of four butterflyfish species in the Chaetodon subgenus Corallochaetodon, with two widespread species (Indian Ocean – C. trifasciatus and Pacific Ocean – C. lunulatus
Understanding Computational Bayesian Statistics
Bolstad, William M
2011-01-01
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistic
Bayesian statistics an introduction
Lee, Peter M
2012-01-01
Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel
Bayesian analysis of rare events
Energy Technology Data Exchange (ETDEWEB)
Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang
2016-06-01
In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.
Directory of Open Access Journals (Sweden)
Hui Wang
Full Text Available Ichthyophis bannanicus is the only caecilian species in China. In this study, the phylogeography and population demography of I. bannanicus were explored, based on the mitochondrial DNA genes (cyt b and ND2 and 15 polymorphic microsatellite loci. Altogether 158 individuals were collected from five populations in Yunnan province, Guangxi province, Guangdong province, and Northern Vietnam. Phylogeographical and population structure analysis identified either two groups (Xishuangbanna, Northern Vietnam-Yulin-Yangchun-Deqing or three groups (Xishuangbanna, Northern Vietnam-Yulin-Yangchun, and Deqing, indicating that the Red River and Pearl River systems may have acted as gene-flow barriers for I. bannanicus. Historical population expansion that happened 15-17 Ka ago was detected for mtDNA data and was possibly triggered by warmer weather after the Last Glacial Maximum. However, the Bayesian simulations of population history based on microsatellite data pinpointed population decline in all populations since 19,123 to 1,029 years ago, demonstrating a significant influence of anthropogenic habitat alteration on I. bannanicus.
Genomic diversity and phylogeography of norovirus in China.
Qiao, Niu; Ren, He; Liu, Lei
2017-10-03
Little is known about the phylogeography of norovirus (NoV) in China. In norovirus, a clear understanding for the characteristics of tree topology, migration patterns and its demographic dynamics in viral circulation are needed to identify its prevalence trends, which can help us better prepare for its epidemics as well as develop useful control strategies. The aim of this study was to explore the genetic diversity, temporal distribution, demographic dynamics and migration patterns of NoV that circulated in China. Our analysis showed that two major genogroups, GI and GII, were identified in China, in which GII.3, GII.4 and GII.17 accounted for the majority with a total proportion around 70%. Our demography inference suggested that during the long-term migration process, NoV evolved into multiple lineages and then experienced a selective sweep, which reduced its genetic diversity. The phylogeography results suggested that the norovirus may have originated form the South China (Hong Kong and Guangdong), followed by multicenter direction outbreaks across the country. From these analyses, we indicate that domestic poultry trade and frequent communications of people from different regions have all contributed to the spread of the NoV in China. Together with recent advances in phylogeographic inference, our researches also provide powerful illustrations of how coalescent-based methods can extract adequate information in molecular epidemiology.
An Intuitive Dashboard for Bayesian Network Inference
International Nuclear Information System (INIS)
Reddy, Vikas; Farr, Anna Charisse; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K D V
2014-01-01
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++
An Intuitive Dashboard for Bayesian Network Inference
Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.
2014-03-01
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.
Bayesian optimization for computationally extensive probability distributions.
Tamura, Ryo; Hukushima, Koji
2018-01-01
An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribution. Our Bayesian optimization technique is applied to the posterior distribution in the effective physical model estimation, which is a computationally extensive probability distribution. Even when the number of sampling points on the posterior distributions is fixed to be small, the Bayesian optimization provides a better maximizer of the posterior distributions in comparison to those by the random search method, the steepest descent method, or the Monte Carlo method. Furthermore, the Bayesian optimization improves the results efficiently by combining the steepest descent method and thus it is a powerful tool to search for a better maximizer of computationally extensive probability distributions.
Yuan, Ying; MacKinnon, David P.
2009-01-01
In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…
Kleibergen, F.R.; Kleijn, R.; Paap, R.
2000-01-01
We propose a novel Bayesian test under a (noninformative) Jeffreys'priorspecification. We check whether the fixed scalar value of the so-calledBayesian Score Statistic (BSS) under the null hypothesis is aplausiblerealization from its known and standardized distribution under thealternative. Unlike
von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo
2014-06-01
Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.
Albert, Jim
2009-01-01
There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The earl
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...
Bayesian data analysis for newcomers.
Kruschke, John K; Liddell, Torrin M
2018-02-01
This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.
Comparative phylogeography in rainforest trees from Lower Guinea, Africa.
Directory of Open Access Journals (Sweden)
Myriam Heuertz
Full Text Available Comparative phylogeography is an effective approach to assess the evolutionary history of biological communities. We used comparative phylogeography in fourteen tree taxa from Lower Guinea (Atlantic Equatorial Africa to test for congruence with two simple evolutionary scenarios based on physio-climatic features 1 the W-E environmental gradient and 2 the N-S seasonal inversion, which determine climatic and seasonality differences in the region. We sequenced the trnC-ycf6 plastid DNA region using a dual sampling strategy: fourteen taxa with small sample sizes (dataset 1, mean n = 16/taxon, to assess whether a strong general pattern of allele endemism and genetic differentiation emerged; and four taxonomically well-studied species with larger sample sizes (dataset 2, mean n = 109/species to detect the presence of particular shared phylogeographic patterns. When grouping the samples into two alternative sets of two populations, W and E, vs. N and S, neither dataset exhibited a strong pattern of allelic endemism, suggesting that none of the considered regions consistently harboured older populations. Differentiation in dataset 1 was similarly strong between W and E as between N and S, with 3-5 significant F ST tests out of 14 tests in each scenario. Coalescent simulations indicated that, given the power of the data, this result probably reflects idiosyncratic histories of the taxa, or a weak common differentiation pattern (possibly with population substructure undetectable across taxa in dataset 1. Dataset 2 identified a common genetic break separating the northern and southern populations of Greenwayodendron suaveolens subsp. suaveolens var. suaveolens, Milicia excelsa, Symphonia globulifera and Trichoscypha acuminata in Lower Guinea, in agreement with differentiation across the N-S seasonal inversion. Our work suggests that currently recognized tree taxa or suspected species complexes can contain strongly differentiated genetic lineages
Phylogeography of Schizopygopsis stoliczkai (Cyprinidae) in Northwest Tibetan Plateau area.
Wanghe, Kunyuan; Tang, Yongtao; Tian, Fei; Feng, Chenguang; Zhang, Renyi; Li, Guogang; Liu, Sijia; Zhao, Kai
2017-11-01
Schizopygopsis stoliczkai (Cyprinidae, subfamily Schizothoracinae) is one of the major freshwater fishes endemic to the northwestern margin of the Tibetan Plateau. In the current study, we used mitochondrial DNA markers cytochrome b (Cyt b ) and 16S rRNA (16S), as well as the nuclear marker, the second intron of the nuclear beta-actin gene (Act2), to uncover the phylogeography of S. stoliczkai . In total, we obtained 74 haplotypes from 403 mitochondrial concatenated sequences. The mtDNA markers depict the phylogenetic structures of S. stoliczkai , which consist of clade North and clade South. The split time of the two clades is dated back to 4.27 Mya (95% HPD = 1.96-8.20 Mya). The estimated split time is earlier than the beginning of the ice age of Pleistocene (2.60 Mya), suggesting that the northwestern area of the Tibetan Plateau probably contain at least two glacial refugia for S. stoliczkai . SAMOVA supports the formation of four groups: (i) the Karakash River group; (ii) The Lake Pangong group; (iii) the Shiquan River group; (iv) the Southern Basin group. Clade North included Karakash River, Lake Pangong, and Shiquan River groups, while seven populations of clade South share the haplotypes. Genetic diversity, star-like network, BSP analysis, as well as negative neutrality tests indicate recent expansions events of S. stoliczkai . Conclusively, our results illustrate the phylogeography of S. stoliczkai , implying the Shiquan River is presumably the main refuge for S. stoliczkai .
Cantanhede, Andréa Martins; Da Silva, Vera Maria Ferreira; Farias, Izeni Pires; Hrbek, Tomas; Lazzarini, Stella Maris; Alves-Gomes, José
2005-02-01
We used mitochondrial DNA control region sequences to examine phylogeography and population differentiation of the endangered Amazonian manatee Trichechus inunguis. We observe lack of molecular differentiation among localities and we find weak association between geographical and genetic distances. However, nested clade analysis supports restricted gene flow and/or dispersal with some long-distance dispersal. Although this species has a history of extensive hunting, genetic diversity and effective population sizes are relatively high when compared to the West Indian manatee Trichechus manatus. Patterns of mtDNA haplotype diversity in T. inunguis suggest a genetic disequilibrium most likely explained by demographic expansion resulting from secession of hunting and enforcement of conservation and protective measures. Phylogenetic analysis of T. manatus and T. inunguis haplotypes suggests that T. inunguis is nested within T. manatus, effectively making T. manatus a paraphyletic entity. Paraphyly of T. manatus and recent divergence times of T. inunguis and the three main T. manatus lineages suggest a possible need for a taxonomic re-evaluation of the western Atlantic Trichechus.
Bayesian methods for data analysis
Carlin, Bradley P.
2009-01-01
Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches The Bayes-Frequentist Controversy Some Basic Bayesian Models The Bayes approach Introduction Prior Distributions Bayesian Inference Hierarchical Modeling Model Assessment Nonparametric Methods Bayesian computation Introduction Asymptotic Methods Noniterative Monte Carlo Methods Markov Chain Monte Carlo Methods Model criticism and selection Bayesian Modeling Bayesian Robustness Model Assessment Bayes Factors via Marginal Density Estimation Bayes Factors
Noncausal Bayesian Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution...
Statistics: a Bayesian perspective
National Research Council Canada - National Science Library
Berry, Donald A
1996-01-01
...: it is the only introductory textbook based on Bayesian ideas, it combines concepts and methods, it presents statistics as a means of integrating data into the significant process, it develops ideas...
Fox, Gerardus J.A.; van den Berg, Stéphanie Martine; Veldkamp, Bernard P.; Irwing, P.; Booth, T.; Hughes, D.
2015-01-01
In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item
Bayesian Networks An Introduction
Koski, Timo
2009-01-01
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include:.: An introduction to Dirichlet Distribution, Exponential Families and their applications.; A detailed description of learni
Maeda, Shin-ichi
2014-01-01
Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of weight parameters and prediction after learning. The experiment result also encourages the optimization of the dropout.
Ghosh, Sujit K
2010-01-01
Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.
Comparative phylogeography of African savannah ungulates
DEFF Research Database (Denmark)
Lorenzen, Eline; Heller, Rasmus; Siegismund, Hans Redlef
2012-01-01
The savannah biome of sub-Saharan Africa harbours the highest diversity of ungulates (hoofed mammals) on Earth. In this review, we compile population genetic data from 19 codistributed ungulate taxa of the savannah biome and find striking concordance in the phylogeographic structuring of species...... and South-West Africa. Furthermore, differing Pleistocene evolutionary biogeographic scenarios are proposed for East and Southern Africa, supported by palaeoclimatic data and the fossil record. Environmental instability in East Africa facilitated several spatial and temporal refugia and is reflected...
Numeracy, frequency, and Bayesian reasoning
Directory of Open Access Journals (Sweden)
Gretchen B. Chapman
2009-02-01
Full Text Available Previous research has demonstrated that Bayesian reasoning performance is improved if uncertainty information is presented as natural frequencies rather than single-event probabilities. A questionnaire study of 342 college students replicated this effect but also found that the performance-boosting benefits of the natural frequency presentation occurred primarily for participants who scored high in numeracy. This finding suggests that even comprehension and manipulation of natural frequencies requires a certain threshold of numeracy abilities, and that the beneficial effects of natural frequency presentation may not be as general as previously believed.
Bayesian networks with examples in R
Scutari, Marco
2014-01-01
Introduction. The Discrete Case: Multinomial Bayesian Networks. The Continuous Case: Gaussian Bayesian Networks. More Complex Cases. Theory and Algorithms for Bayesian Networks. Real-World Applications of Bayesian Networks. Appendices. Bibliography.
Comparative phylogeography of endemic Azorean arthropods.
Parmakelis, Aristeidis; Rigal, François; Mourikis, Thanos; Balanika, Katerina; Terzopoulou, Sofia; Rego, Carla; Amorim, Isabel R; Crespo, Luís; Pereira, Fernando; Triantis, Kostas A; Whittaker, Robert J; Borges, Paulo A V
2015-11-11
For a remote oceanic archipelago of up to 8 Myr age, the Azores have a comparatively low level of endemism. We present an analysis of phylogeographic patterns of endemic Azorean island arthropods aimed at testing patterns of diversification in relation to the ontogeny of the archipelago, in order to distinguish between alternative models of evolutionary dynamics on islands. We collected individuals of six species (representing Araneae, Hemiptera and Coleoptera) from 16 forest fragments from 7 islands. Using three mtDNA markers, we analysed the distribution of genetic diversity within and between islands, inferred the differentiation time-frames and investigated the inter-island migration routes and colonization patterns. Each species exhibited very low levels of mtDNA divergence, both within and between islands. The two oldest islands were not strongly involved in the diffusion of genetic diversity within the archipelago. The most haplotype-rich islands varied according to species but the younger, central islands contributed the most to haplotype diversity. Colonization events both in concordance with and in contradiction to an inter-island progression rule were inferred, while a non-intuitive pattern of colonization from western to eastern islands was also inferred. The geological development of the Azores has followed a less tidy progression compared to classic hotspot archipelagos, and this is reflected in our findings. The study species appear to have been differentiating within the Azores for <2 Myr, a fraction of the apparent life span of the archipelago, which may indicate that extinction events linked to active volcanism have played an important role. Assuming that after each extinction event, colonization was initiated from a nearby island hosting derived haplotypes, the apparent age of species diversification in the archipelago would be moved closer to the present after each extinction-recolonization cycle. Exploiting these ideas, we propose a general
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...
Bayesian methods in reliability
Sander, P.; Badoux, R.
1991-11-01
The present proceedings from a course on Bayesian methods in reliability encompasses Bayesian statistical methods and their computational implementation, models for analyzing censored data from nonrepairable systems, the traits of repairable systems and growth models, the use of expert judgment, and a review of the problem of forecasting software reliability. Specific issues addressed include the use of Bayesian methods to estimate the leak rate of a gas pipeline, approximate analyses under great prior uncertainty, reliability estimation techniques, and a nonhomogeneous Poisson process. Also addressed are the calibration sets and seed variables of expert judgment systems for risk assessment, experimental illustrations of the use of expert judgment for reliability testing, and analyses of the predictive quality of software-reliability growth models such as the Weibull order statistics.
CSIR Research Space (South Africa)
Rosman, Benjamin
2016-02-01
Full Text Available Keywords Policy Reuse · Reinforcement Learning · Online Learning · Online Bandits · Transfer Learning · Bayesian Optimisation · Bayesian Decision Theory. 1 Introduction As robots and software agents are becoming more ubiquitous in many applications.... The agent has access to a library of policies (pi1, pi2 and pi3), and has previously experienced a set of task instances (τ1, τ2, τ3, τ4), as well as samples of the utilities of the library policies on these instances (the black dots indicate the means...
Czech Academy of Sciences Publication Activity Database
Kindler, C.; Böhme, W.; Corti, C.; Gvoždík, Václav; Jablonski, D.; Jandzik, D.; Metallinou, M.; Široký, P.; Fritz, U.
2013-01-01
Roč. 42, č. 5 (2013), s. 458-472 ISSN 0300-3256 Institutional support: RVO:67985904 Keywords : TURTLES EMYS-ORBICULARIS * NUCLEAR-DNA SEQUENCES * MOLECULAR PHYLOGEOGRAPHY Subject RIV: EG - Zoology Impact factor: 2.922, year: 2013
Contrasting phylogeography of two Western Palaearctic fish parasites despite similar life cycles
Czech Academy of Sciences Publication Activity Database
Perrot-Minnot, M. J.; Špakulová, M.; Wattier, R.; Kotlík, Petr; Düsen, S.; Aydoğdu, A.; Tougard, C.
2018-01-01
Roč. 45, č. 1 (2018), s. 101-115 ISSN 0305-0270 R&D Projects: GA MŠk EF15_003/0000460 Institutional support: RVO:67985904 Keywords : amphipod * British Islands * comparative phylogeography * Cyprinidae * Danube Subject RIV: EG - Zoology Impact factor: 4.248, year: 2016
Bayesian methods for hackers probabilistic programming and Bayesian inference
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...
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Korattikara, A.; Rathod, V.; Murphy, K.; Welling, M.; Cortes, C.; Lawrence, N.D.; Lee, D.D.; Sugiyama, M.; Garnett, R.
2015-01-01
We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities p(y|x, D), e.g., for applications involving bandits or active learning. One simple
Bayesian Geostatistical Design
DEFF Research Database (Denmark)
Diggle, Peter; Lophaven, Søren Nymand
2006-01-01
locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model...
DEFF Research Database (Denmark)
Hartelius, Karsten; Carstensen, Jens Michael
2003-01-01
A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov random field (MRF) model which r...
Bayesian Independent Component Analysis
DEFF Research Database (Denmark)
Winther, Ole; Petersen, Kaare Brandt
2007-01-01
In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...
Bayesian Exponential Smoothing.
Forbes, C.S.; Snyder, R.D.; Shami, R.S.
2000-01-01
In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows us to improve current practices surrounding exponential smoothing by providing both point predictions and measures of the uncertainty surrounding them.
Directory of Open Access Journals (Sweden)
Shinya Sato
Full Text Available BACKGROUND: A dinoflagellate genus Ostreopsis is known as a potential producer of Palytoxin derivatives. Palytoxin is the most potent non-proteinaceous compound reported so far. There has been a growing number of reports on palytoxin-like poisonings in southern areas of Japan; however, the distribution of Ostreopsis has not been investigated so far. Morphological plasticity of Ostreopsis makes reliable microscopic identification difficult so the employment of molecular tools was desirable. METHODS/PRINCIPAL FINDING: In total 223 clones were examined from samples mainly collected from southern areas of Japan. The D8-D10 region of the nuclear large subunit rDNA (D8-D10 was selected as a genetic marker and phylogenetic analyses were conducted. Although most of the clones were unable to be identified, there potentially 8 putative species established during this study. Among them, Ostreopsis sp. 1-5 did not belong to any known clade, and each of them formed its own clade. The dominant species was Ostreopsis sp. 1, which accounted for more than half of the clones and which was highly toxic and only distributed along the Japanese coast. Comparisons between the D8-D10 and the Internal Transcribed Spacer (ITS region of the nuclear rDNA, which has widely been used for phylogenetic/phylogeographic studies in Ostreopsis, revealed that the D8-D10 was less variable than the ITS, making consistent and reliable phylogenetic reconstruction possible. CONCLUSIONS/SIGNIFICANCE: This study unveiled a surprisingly diverse and widespread distribution of Japanese Ostreopsis. Further study will be required to better understand the phylogeography of the genus. Our results posed the urgent need for the development of the early detection/warning systems for Ostreopsis, particularly for the widely distributed and strongly toxic Ostreopsis sp. 1. The D8-D10 marker will be suitable for these purposes.
Garcia-Rodriguez, A I; Bowen, B W; Domning, D; Mignucci-Giannoni, A; Marmontel, M; Montoya-Ospina, A; Morales-Vela, B; Rudin, M; Bonde, R K; McGuire, P M
1998-09-01
To resolve the population genetic structure and phylogeography of the West Indian manatee (Trichechus manatus), mitochondrial (mt) DNA control region sequences were compared among eight locations across the western Atlantic region. Fifteen haplotypes were identified among 86 individuals from Florida, Puerto Rico, the Dominican Republic, Mexico, Columbia, Venezuela, Guyana and Brazil. Despite the manatee's ability to move thousands of kilometers along continental margins, strong population separations between most locations were demonstrated with significant haplotype frequency shifts. These findings are consistent with tagging studies which indicate that stretches of open water and unsuitable coastal habitats constitute substantial barriers to gene flow and colonization. Low levels of genetic diversity within Florida and Brazilian samples might be explained by recent colonization into high latitudes or bottleneck effects. Three distinctive mtDNA lineages were observed in an intraspecific phylogeny of T. manatus, corresponding approximately to: (i) Florida and the West Indies; (ii) the Gulf of Mexico to the Caribbean rivers of South America; and (iii) the northeast Atlantic coast of South America. These lineages, which are not concordant with previous subspecies designations, are separated by sequence divergence estimates of d = 0.04-0.07, approximately the same level of divergence observed between T. manatus and the Amazonian manatee (T. inunguis, n = 16). Three individuals from Guyana, identified as T. manatus, had mtDNA haplotypes which are affiliated with the endemic Amazon form T. inunguis. The three primary T. manatus lineages and the T. inunguis lineage may represent relatively deep phylogeographic partitions which have been bridged recently due to changes in habitat availability (after the Wisconsin glacial period, 10 000 B P), natural colonization, and human-mediated transplantation.
Garcia-Rodriguez, A. I.; Bowen, B.W.; Domning, D.; Mignucci-Giannoni, A. A.; Marmontel, M.; Montoya-Ospina, R. A.; Morales-Vela, B.; Rudin, M.; Bonde, R.K.; McGuire, P.M.
1998-01-01
To resolve the population genetic structure and phylogeography of the West Indian manatee (Trichechus manatus), mitochondrial (mt) DNA control region sequences were compared among eight locations across the western Atlantic region. Fifteen haplotypes were identified among 86 individuals from Florida, Puerto Rico, the Dominican Republic, Mexico, Colombia, Venezuela, Guyana and Brazil. Despite the manatee's ability to move thousands of kilometres along continental margins, strong population separations between most locations were demonstrated with significant haplotype frequency shifts. These findings are consistent with tagging studies which indicate that stretches of open water and unsuitable coastal habitats constitute substantial barriers to gene flow and colonization. Low levels of genetic diversity within Florida and Brazilian samples might be explained by recent colonization into high latitudes or bottleneck effects. Three distinctive mtDNA lineages were observed in an intraspecific phylogeny of T. manatus, corresponding approximately to: (i) Florida and the West Indies; (ii) the Gulf of Mexico to the Caribbean rivers of South America; and (iii) the northeast Atlantic coast of South America. These lineages, which are not concordant with previous subspecies designations, are separated by sequence divergence estimates of d = 0.04-0.07, approximately the same level of divergence observed between T. manatus and the Amazonian manatee (T. inunguis, n = 16). Three individuals from Guyana, identified as T. manatus, had mtDNA haplotypes which are affiliated with the endemic Amazon form T. inunguis. The three primary T. manatus lineages and the T. inunguis lineage may represent relatively deep phylogeographic partitions which have been bridged recently due to changes in habitat availability (after the Wisconsin glacial period, 10 000 BP), natural colonization, and human-mediated transplantation.
Optimal Detection under the Restricted Bayesian Criterion
Directory of Open Access Journals (Sweden)
Shujun Liu
2017-07-01
Full Text Available This paper aims to find a suitable decision rule for a binary composite hypothesis-testing problem with a partial or coarse prior distribution. To alleviate the negative impact of the information uncertainty, a constraint is considered that the maximum conditional risk cannot be greater than a predefined value. Therefore, the objective of this paper becomes to find the optimal decision rule to minimize the Bayes risk under the constraint. By applying the Lagrange duality, the constrained optimization problem is transformed to an unconstrained optimization problem. In doing so, the restricted Bayesian decision rule is obtained as a classical Bayesian decision rule corresponding to a modified prior distribution. Based on this transformation, the optimal restricted Bayesian decision rule is analyzed and the corresponding algorithm is developed. Furthermore, the relation between the Bayes risk and the predefined value of the constraint is also discussed. The Bayes risk obtained via the restricted Bayesian decision rule is a strictly decreasing and convex function of the constraint on the maximum conditional risk. Finally, the numerical results including a detection example are presented and agree with the theoretical results.
Bayesian optimization for materials science
Packwood, Daniel
2017-01-01
This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science. Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While re...
Natanegara, Fanni; Neuenschwander, Beat; Seaman, John W; Kinnersley, Nelson; Heilmann, Cory R; Ohlssen, David; Rochester, George
2014-01-01
Bayesian applications in medical product development have recently gained popularity. Despite many advances in Bayesian methodology and computations, increase in application across the various areas of medical product development has been modest. The DIA Bayesian Scientific Working Group (BSWG), which includes representatives from industry, regulatory agencies, and academia, has adopted the vision to ensure Bayesian methods are well understood, accepted more broadly, and appropriately utilized to improve decision making and enhance patient outcomes. As Bayesian applications in medical product development are wide ranging, several sub-teams were formed to focus on various topics such as patient safety, non-inferiority, prior specification, comparative effectiveness, joint modeling, program-wide decision making, analytical tools, and education. The focus of this paper is on the recent effort of the BSWG Education sub-team to administer a Bayesian survey to statisticians across 17 organizations involved in medical product development. We summarize results of this survey, from which we provide recommendations on how to accelerate progress in Bayesian applications throughout medical product development. The survey results support findings from the literature and provide additional insight on regulatory acceptance of Bayesian methods and information on the need for a Bayesian infrastructure within an organization. The survey findings support the claim that only modest progress in areas of education and implementation has been made recently, despite substantial progress in Bayesian statistical research and software availability. Copyright © 2013 John Wiley & Sons, Ltd.
Opatova, Vera; Arnedo, Miquel A.
2014-01-01
Studies conducted on volcanic islands have greatly contributed to our current understanding of how organisms diversify. The Canary Islands archipelago, located northwest of the coast of northern Africa, harbours a large number of endemic taxa. Because of their low vagility, mygalomorph spiders are usually absent from oceanic islands. The spider Titanidiops canariensis, which inhabits the easternmost islands of the archipelago, constitutes an exception to this rule. Here, we use a multi-locus approach that combines three mitochondrial and four nuclear genes to investigate the origins and phylogeography of this remarkable trap-door spider. We provide a timeframe for the colonisation of the Canary Islands using two alternative approaches: concatenation and species tree inference in a Bayesian relaxed clock framework. Additionally, we investigate the existence of cryptic species on the islands by means of a Bayesian multi-locus species delimitation method. Our results indicate that T. canariensis colonised the Canary Islands once, most likely during the Miocene, although discrepancies between the timeframes from different approaches make the exact timing uncertain. A complex evolutionary history for the species in the archipelago is revealed, which involves two independent colonisations of Fuerteventura from the ancestral range of T. canariensis in northern Lanzarote and a possible back colonisation of southern Lanzarote. The data further corroborate a previously proposed volcanic refugium, highlighting the impact of the dynamic volcanic history of the island on the phylogeographic patterns of the endemic taxa. T. canariensis includes at least two different species, one inhabiting the Jandia peninsula and central Fuerteventura and one spanning from central Fuerteventura to Lanzarote. Our data suggest that the extant northern African Titanidiops lineages may have expanded to the region after the islands were colonised and, hence, are not the source of colonisation. In
Directory of Open Access Journals (Sweden)
Vera Opatova
Full Text Available Studies conducted on volcanic islands have greatly contributed to our current understanding of how organisms diversify. The Canary Islands archipelago, located northwest of the coast of northern Africa, harbours a large number of endemic taxa. Because of their low vagility, mygalomorph spiders are usually absent from oceanic islands. The spider Titanidiops canariensis, which inhabits the easternmost islands of the archipelago, constitutes an exception to this rule. Here, we use a multi-locus approach that combines three mitochondrial and four nuclear genes to investigate the origins and phylogeography of this remarkable trap-door spider. We provide a timeframe for the colonisation of the Canary Islands using two alternative approaches: concatenation and species tree inference in a Bayesian relaxed clock framework. Additionally, we investigate the existence of cryptic species on the islands by means of a Bayesian multi-locus species delimitation method. Our results indicate that T. canariensis colonised the Canary Islands once, most likely during the Miocene, although discrepancies between the timeframes from different approaches make the exact timing uncertain. A complex evolutionary history for the species in the archipelago is revealed, which involves two independent colonisations of Fuerteventura from the ancestral range of T. canariensis in northern Lanzarote and a possible back colonisation of southern Lanzarote. The data further corroborate a previously proposed volcanic refugium, highlighting the impact of the dynamic volcanic history of the island on the phylogeographic patterns of the endemic taxa. T. canariensis includes at least two different species, one inhabiting the Jandia peninsula and central Fuerteventura and one spanning from central Fuerteventura to Lanzarote. Our data suggest that the extant northern African Titanidiops lineages may have expanded to the region after the islands were colonised and, hence, are not the source
Opatova, Vera; Arnedo, Miquel A
2014-01-01
Studies conducted on volcanic islands have greatly contributed to our current understanding of how organisms diversify. The Canary Islands archipelago, located northwest of the coast of northern Africa, harbours a large number of endemic taxa. Because of their low vagility, mygalomorph spiders are usually absent from oceanic islands. The spider Titanidiops canariensis, which inhabits the easternmost islands of the archipelago, constitutes an exception to this rule. Here, we use a multi-locus approach that combines three mitochondrial and four nuclear genes to investigate the origins and phylogeography of this remarkable trap-door spider. We provide a timeframe for the colonisation of the Canary Islands using two alternative approaches: concatenation and species tree inference in a Bayesian relaxed clock framework. Additionally, we investigate the existence of cryptic species on the islands by means of a Bayesian multi-locus species delimitation method. Our results indicate that T. canariensis colonised the Canary Islands once, most likely during the Miocene, although discrepancies between the timeframes from different approaches make the exact timing uncertain. A complex evolutionary history for the species in the archipelago is revealed, which involves two independent colonisations of Fuerteventura from the ancestral range of T. canariensis in northern Lanzarote and a possible back colonisation of southern Lanzarote. The data further corroborate a previously proposed volcanic refugium, highlighting the impact of the dynamic volcanic history of the island on the phylogeographic patterns of the endemic taxa. T. canariensis includes at least two different species, one inhabiting the Jandia peninsula and central Fuerteventura and one spanning from central Fuerteventura to Lanzarote. Our data suggest that the extant northern African Titanidiops lineages may have expanded to the region after the islands were colonised and, hence, are not the source of colonisation. In
Probability and Bayesian statistics
1987-01-01
This book contains selected and refereed contributions to the "Inter national Symposium on Probability and Bayesian Statistics" which was orga nized to celebrate the 80th birthday of Professor Bruno de Finetti at his birthplace Innsbruck in Austria. Since Professor de Finetti died in 1985 the symposium was dedicated to the memory of Bruno de Finetti and took place at Igls near Innsbruck from 23 to 26 September 1986. Some of the pa pers are published especially by the relationship to Bruno de Finetti's scientific work. The evolution of stochastics shows growing importance of probability as coherent assessment of numerical values as degrees of believe in certain events. This is the basis for Bayesian inference in the sense of modern statistics. The contributions in this volume cover a broad spectrum ranging from foundations of probability across psychological aspects of formulating sub jective probability statements, abstract measure theoretical considerations, contributions to theoretical statistics an...
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel N
2012-01-01
Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....
Approximate Bayesian recursive estimation
Czech Academy of Sciences Publication Activity Database
Kárný, Miroslav
2014-01-01
Roč. 285, č. 1 (2014), s. 100-111 ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Approximate parameter estimation * Bayesian recursive estimation * Kullback–Leibler divergence * Forgetting Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.038, year: 2014 http://library.utia.cas.cz/separaty/2014/AS/karny-0425539.pdf
DEFF Research Database (Denmark)
Antoniou, Constantinos; Harrison, Glenn W.; Lau, Morten I.
2015-01-01
A large literature suggests that many individuals do not apply Bayes’ Rule when making decisions that depend on them correctly pooling prior information and sample data. We replicate and extend a classic experimental study of Bayesian updating from psychology, employing the methods of experimenta...... economics, with careful controls for the confounding effects of risk aversion. Our results show that risk aversion significantly alters inferences on deviations from Bayes’ Rule....
Energy Technology Data Exchange (ETDEWEB)
Andrews, Stephen A. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sigeti, David E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-11-15
These are a set of slides about Bayesian hypothesis testing, where many hypotheses are tested. The conclusions are the following: The value of the Bayes factor obtained when using the median of the posterior marginal is almost the minimum value of the Bayes factor. The value of τ^{2} which minimizes the Bayes factor is a reasonable choice for this parameter. This allows a likelihood ratio to be computed with is the least favorable to H_{0}.
Introduction to Bayesian statistics
Koch, Karl-Rudolf
2007-01-01
This book presents Bayes' theorem, the estimation of unknown parameters, the determination of confidence regions and the derivation of tests of hypotheses for the unknown parameters. It does so in a simple manner that is easy to comprehend. The book compares traditional and Bayesian methods with the rules of probability presented in a logical way allowing an intuitive understanding of random variables and their probability distributions to be formed.
Transcontinental phylogeography of the Daphnia pulex species complex.
Crease, Teresa J; Omilian, Angela R; Costanzo, Katie S; Taylor, Derek J
2012-01-01
Daphnia pulex is quickly becoming an attractive model species in the field of ecological genomics due to the recent release of its complete genome sequence, a wide variety of new genomic resources, and a rich history of ecological data. Sequences of the mitochondrial NADH dehydrogenase subunit 5 and cytochrome c oxidase subunit 1 genes were used to assess the global phylogeography of this species, and to further elucidate its phylogenetic relationship to other members of the Daphnia pulex species complex. Using both newly acquired and previously published data, we analyzed 398 individuals from collections spanning five continents. Eleven strongly supported lineages were found within the D. pulex complex, and one lineage in particular, panarctic D. pulex, has very little phylogeographical structure and a near worldwide distribution. Mismatch distribution, haplotype network, and population genetic analyses are compatible with a North American origin for this lineage and subsequent spatial expansion in the Late Pleistocene. In addition, our analyses suggest that dispersal between North and South America of this and other species in the D. pulex complex has occurred multiple times, and is predominantly from north to south. Our results provide additional support for the evolutionary relationships of the eleven main mitochondrial lineages of the D. pulex complex. We found that the well-studied panarctic D. pulex is present on every continent except Australia and Antarctica. Despite being geographically very widespread, there is a lack of strong regionalism in the mitochondrial genomes of panarctic D. pulex--a pattern that differs from that of most studied cladocerans. Moreover, our analyses suggest recent expansion of the panarctic D. pulex lineage, with some continents sharing haplotypes. The hypothesis that hybrid asexuality has contributed to the recent and unusual geographic success of the panarctic D. pulex lineage warrants further study.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bayesian theory and applications
Dellaportas, Petros; Polson, Nicholas G; Stephens, David A
2013-01-01
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...
Directory of Open Access Journals (Sweden)
Pablo Fresia
Full Text Available Insect pest phylogeography might be shaped both by biogeographic events and by human influence. Here, we conducted an approximate Bayesian computation (ABC analysis to investigate the phylogeography of the New World screwworm fly, Cochliomyia hominivorax, with the aim of understanding its population history and its order and time of divergence. Our ABC analysis supports that populations spread from North to South in the Americas, in at least two different moments. The first split occurred between the North/Central American and South American populations in the end of the Last Glacial Maximum (15,300-19,000 YBP. The second split occurred between the North and South Amazonian populations in the transition between the Pleistocene and the Holocene eras (9,100-11,000 YBP. The species also experienced population expansion. Phylogenetic analysis likewise suggests this north to south colonization and Maxent models suggest an increase in the number of suitable areas in South America from the past to present. We found that the phylogeographic patterns observed in C. hominivorax cannot be explained only by climatic oscillations and can be connected to host population histories. Interestingly we found these patterns are very coincident with general patterns of ancient human movements in the Americas, suggesting that humans might have played a crucial role in shaping the distribution and population structure of this insect pest. This work presents the first hypothesis test regarding the processes that shaped the current phylogeographic structure of C. hominivorax and represents an alternate perspective on investigating the problem of insect pests.
Bayes Academy - An Educational Game for Learning Bayesian Networks
Sotala, Kaj
2015-01-01
This thesis describes the development of 'Bayes Academy', an educational game which aims to teach an understanding of Bayesian networks. A Bayesian network is a directed acyclic graph describing a joint probability distribution function over n random variables, where each node in the graph represents a random variable. To find a way to turn this subject into an interesting game, this work draws on the theoretical background of meaningful play. Among other requirements, actions in the game...
An Analysis of Construction Accident Factors Based on Bayesian Network
Yunsheng Zhao; Jinyong Pei
2013-01-01
In this study, we have an analysis of construction accident factors based on bayesian network. Firstly, accidents cases are analyzed to build Fault Tree method, which is available to find all the factors causing the accidents, then qualitatively and quantitatively analyzes the factors with Bayesian network method, finally determines the safety management program to guide the safety operations. The results of this study show that bad condition of geological environment has the largest posterio...
Bayesian analysis in plant pathology.
Mila, A L; Carriquiry, A L
2004-09-01
ABSTRACT Bayesian methods are currently much discussed and applied in several disciplines from molecular biology to engineering. Bayesian inference is the process of fitting a probability model to a set of data and summarizing the results via probability distributions on the parameters of the model and unobserved quantities such as predictions for new observations. In this paper, after a short introduction of Bayesian inference, we present the basic features of Bayesian methodology using examples from sequencing genomic fragments and analyzing microarray gene-expressing levels, reconstructing disease maps, and designing experiments.
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Congdon, Peter
2014-01-01
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBU
Czech Academy of Sciences Publication Activity Database
Kodandaramaiah, U.; Konvička, Martin; Tammaru, T.; Wahlberg, N.; Gotthard, K.
2012-01-01
Roč. 16, č. 2 (2012), s. 305-313 ISSN 1366-638X R&D Projects: GA MŠk LC06073; GA ČR GAP505/10/2167 Institutional support: RVO:60077344 Keywords : Lopinga achine * phylogeography * conservation Subject RIV: EH - Ecology, Behaviour Impact factor: 1.801, year: 2012 http://link.springer.com/article/10.1007/s10841-012-9465-4?null
Of mice and (Viking?) men: phylogeography of British and Irish house mice
Searle, Jeremy B.; Jones, Catherine S.; Gündüz, İslam; Scascitelli, Moira; Jones, Eleanor P.; Herman, Jeremy S.; Rambau, R. Victor; Noble, Leslie R.; Berry, R.J.; Giménez, Mabel D.; Jóhannesdóttir, Fríða
2008-01-01
The west European subspecies of house mouse (Mus musculus domesticus) has gained much of its current widespread distribution through commensalism with humans. This means that the phylogeography of M. m. domesticus should reflect patterns of human movements. We studied restriction fragment length polymorphism (RFLP) and DNA sequence variations in mouse mitochondrial (mt) DNA throughout the British Isles (328 mice from 105 localities, including previously published data). There is a major mtDNA...
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....
Searching Algorithm Using Bayesian Updates
Caudle, Kyle
2010-01-01
In late October 1967, the USS Scorpion was lost at sea, somewhere between the Azores and Norfolk Virginia. Dr. Craven of the U.S. Navy's Special Projects Division is credited with using Bayesian Search Theory to locate the submarine. Bayesian Search Theory is a straightforward and interesting application of Bayes' theorem which involves searching…
Bayesian Data Analysis (lecture 2)
CERN. Geneva
2018-01-01
framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.
Bayesian Data Analysis (lecture 1)
CERN. Geneva
2018-01-01
framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.
Bayesian network learning for natural hazard assessments
Vogel, Kristin
2016-04-01
Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables
The Bayesian Covariance Lasso.
Khondker, Zakaria S; Zhu, Hongtu; Chu, Haitao; Lin, Weili; Ibrahim, Joseph G
2013-04-01
Estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints has drawn a lot of attention in recent years. The abundance of high-dimensional data, where the sample size ( n ) is less than the dimension ( d ), requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore, when n is larger than d but not sufficiently larger, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods, whereas Bayesian approaches rely on matrix decompositions or Wishart priors for shrinkage. In this paper we propose a new method, called the Bayesian Covariance Lasso (BCLASSO), for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases, develop a Bayes estimator for the precision matrix, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data.
Bayesian dynamic mediation analysis.
Huang, Jing; Yuan, Ying
2017-12-01
Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Approximate Bayesian computation.
Directory of Open Access Journals (Sweden)
Mikael Sunnåker
Full Text Available Approximate Bayesian computation (ABC constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology.
Bayesian inference with ecological applications
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...
Bayesian Inference on Gravitational Waves
Directory of Open Access Journals (Sweden)
Asad Ali
2015-12-01
Full Text Available The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.
The NIFTY way of Bayesian signal inference
International Nuclear Information System (INIS)
Selig, Marco
2014-01-01
We introduce NIFTY, 'Numerical Information Field Theory', a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTY can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTY as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D 3 PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy
The NIFTy way of Bayesian signal inference
Selig, Marco
2014-12-01
We introduce NIFTy, "Numerical Information Field Theory", a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTy can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTy as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D3PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy.
Borsboom, D.; Haig, B.D.
2013-01-01
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science
Directory of Open Access Journals (Sweden)
Marta Vila
Full Text Available Genetic isolation and drift may imperil peripheral populations of wide-ranging species more than central ones. Therefore, information about species genetic variability and population structure is invaluable for conservation managers. The Iberian populations of three-spined stickleback lie at the southwestern periphery of the European distribution of Gasterosteus aculeatus. This teleost is a protected species in Portugal and Spain and local extinctions have been reported in both countries during the last decades. Our objectives were (i to determine whether the Iberian populations of G. aculeatus are unique or composed of any of the major evolutionary lineages previously identified and (ii to assess the evolutionary potential of these peripheral populations. We genotyped 478 individuals from 17 sites at 10 polymorphic microsatellite loci to evaluate the genetic variability and differentiation of the Ibero-Balearic populations. We also sequenced 1,165 bp of the mitochondrial genome in 331 of those individuals in order to complement the estimates of genetic diversity in the Ibero-Balearic region. We predicted the evolutionary potential of the different sites analysed based on the contribution of each of them to total allelic/mitochondrial diversity. An intraspecific phylogeny at European level was reconstructed using our data from the mitochondrial cytochrome b gene (755 bp and published sequences. The so-called Transatlantic, European and Mediterranean mitochondrial lineages were found to be present in the Ibero-Balearic region. Their phylogeography suggests a history of multiple colonisations. The nuclear results show, however, a strong correlation between population structure and drainage system. The following basins should be prioritised by conservation policies in order to preserve those populations with the highest evolutionary potential: the Portuguese Vouga and Tagus as well as the Spanish Majorca and Limia. Maintenance of their connectivity
Vila, Marta; Hermida, Miguel; Fernández, Carlos; Perea, Silvia; Doadrio, Ignacio; Amaro, Rafaela; San Miguel, Eduardo
2017-01-01
Genetic isolation and drift may imperil peripheral populations of wide-ranging species more than central ones. Therefore, information about species genetic variability and population structure is invaluable for conservation managers. The Iberian populations of three-spined stickleback lie at the southwestern periphery of the European distribution of Gasterosteus aculeatus. This teleost is a protected species in Portugal and Spain and local extinctions have been reported in both countries during the last decades. Our objectives were (i) to determine whether the Iberian populations of G. aculeatus are unique or composed of any of the major evolutionary lineages previously identified and (ii) to assess the evolutionary potential of these peripheral populations. We genotyped 478 individuals from 17 sites at 10 polymorphic microsatellite loci to evaluate the genetic variability and differentiation of the Ibero-Balearic populations. We also sequenced 1,165 bp of the mitochondrial genome in 331 of those individuals in order to complement the estimates of genetic diversity in the Ibero-Balearic region. We predicted the evolutionary potential of the different sites analysed based on the contribution of each of them to total allelic/mitochondrial diversity. An intraspecific phylogeny at European level was reconstructed using our data from the mitochondrial cytochrome b gene (755 bp) and published sequences. The so-called Transatlantic, European and Mediterranean mitochondrial lineages were found to be present in the Ibero-Balearic region. Their phylogeography suggests a history of multiple colonisations. The nuclear results show, however, a strong correlation between population structure and drainage system. The following basins should be prioritised by conservation policies in order to preserve those populations with the highest evolutionary potential: the Portuguese Vouga and Tagus as well as the Spanish Majorca and Limia. Maintenance of their connectivity, control of
International Nuclear Information System (INIS)
Rajabalinejad, M.
2010-01-01
To reduce cost of Monte Carlo (MC) simulations for time-consuming processes, Bayesian Monte Carlo (BMC) is introduced in this paper. The BMC method reduces number of realizations in MC according to the desired accuracy level. BMC also provides a possibility of considering more priors. In other words, different priors can be integrated into one model by using BMC to further reduce cost of simulations. This study suggests speeding up the simulation process by considering the logical dependence of neighboring points as prior information. This information is used in the BMC method to produce a predictive tool through the simulation process. The general methodology and algorithm of BMC method are presented in this paper. The BMC method is applied to the simplified break water model as well as the finite element model of 17th Street Canal in New Orleans, and the results are compared with the MC and Dynamic Bounds methods.
Bayesian nonparametric hierarchical modeling.
Dunson, David B
2009-04-01
In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.
Book review: Bayesian analysis for population ecology
Link, William A.
2011-01-01
Brian Dennis described the field of ecology as “fertile, uncolonized ground for Bayesian ideas.” He continued: “The Bayesian propagule has arrived at the shore. Ecologists need to think long and hard about the consequences of a Bayesian ecology. The Bayesian outlook is a successful competitor, but is it a weed? I think so.” (Dennis 2004)
Current trends in Bayesian methodology with applications
Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia
2015-01-01
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on
Bayesian image restoration, using configurations
Thorarinsdottir, Thordis
2006-01-01
In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the re...
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification......, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended...
Mitochondrial diversity and phylogeography of Acrossocheilus paradoxus (Teleostei: Cyprinidae).
Ju, Yu-Min; Hsu, Kui-Ching; Yang, Jin-Quan; Wu, Jui-Hsien; Li, Shan; Wang, Wei-Kuang; Ding, Fang; Li, Jun; Lin, Hung-Du
2018-01-31
Mitochondrial DNA cytochrome b sequences (1141 bp) in 229 specimens of Acrossocheilus paradoxus from 26 populations were identified as four lineages. The pairwise genetic distances among these four lineages ranged from 1.57 to 2.37% (mean= 2.00%). Statistical dispersal-vicariance analysis suggests that the ancestral populations were distributed over mainland China and Northern and Western Taiwan. Approximate Bayesian computation approaches show that the three lineages in Taiwan originated from the lineage in mainland China through three colonization routes during two glaciations. The results indicated that during the glaciation and inter-glacial periods, the Taiwan Strait was exposed and sank, which contributed to the dispersion and differentiation of populations. Furthermore, the populations of A. paradoxus colonized Taiwan through a land bridge to the north of the Formosa Bank, and the Miaoli Plateau in Taiwan was an important barrier that limited gene exchange between populations on both the sides.
Cirés, Samuel; Wörmer, Lars; Ballot, Andreas; Agha, Ramsy; Wiedner, Claudia; Velázquez, David; Casero, María Cristina; Quesada, Antonio
2014-02-01
Planktonic Nostocales cyanobacteria represent a challenge for microbiological research because of the wide range of cyanotoxins that they synthesize and their invasive behavior, which is presumably enhanced by global warming. To gain insight into the phylogeography of potentially toxic Nostocales from Mediterranean Europe, 31 strains of Anabaena (Anabaena crassa, A. lemmermannii, A. mendotae, and A. planctonica), Aphanizomenon (Aphanizomenon gracile, A. ovalisporum), and Cylindrospermopsis raciborskii were isolated from 14 freshwater bodies in Spain and polyphasically analyzed for their phylogeography, cyanotoxin production, and the presence of cyanotoxin biosynthesis genes. The potent cytotoxin cylindrospermopsin (CYN) was produced by all 6 Aphanizomenon ovalisporum strains at high levels (5.7 to 9.1 μg CYN mg(-1) [dry weight]) with low variation between strains (1.5 to 3.9-fold) and a marked extracellular release (19 to 41% dissolved CYN) during exponential growth. Paralytic shellfish poisoning (PSP) neurotoxins (saxitoxin, neosaxitoxin, and decarbamoylsaxitoxin) were detected in 2 Aphanizomenon gracile strains, both containing the sxtA gene. This gene was also amplified in non-PSP toxin-producing Aphanizomenon gracile and Aphanizomenon ovalisporum. Phylogenetic analyses supported the species identification and confirmed the high similarity of Spanish Anabaena and Aphanizomenon strains with other European strains. In contrast, Cylindrospermopsis raciborskii from Spain grouped together with American strains and was clearly separate from the rest of the European strains, raising questions about the current assumptions of the phylogeography and spreading routes of C. raciborskii. The present study confirms that the nostocalean genus Aphanizomenon is a major source of CYN and PSP toxins in Europe and demonstrates the presence of the sxtA gene in CYN-producing Aphanizomenon ovalisporum.
Czech Academy of Sciences Publication Activity Database
Hahn, M.W.; Koll, U.; Jezberová, Jitka; Camacho, A.
2015-01-01
Roč. 17, č. 3 (2015), s. 829-840 ISSN 1462-2912 R&D Projects: GA ČR(CZ) GEEEF/10/E011 Institutional support: RVO:60077344 Keywords : polynucleobacter * phylogeography * microbiology * bacteria Subject RIV: EE - Microbiology, Virology Impact factor: 5.932, year: 2015
Czech Academy of Sciences Publication Activity Database
Rodríguez, F.; Hammer, S.; Pérez, T.; Suchentrunk, F.; Lorenzini, R.; Michallet, J.; Martínková, Natália; Albornoz, J.; Domínguez, A.
2009-01-01
Roč. 100, č. 1 (2009), s. 47-55 ISSN 0022-1503 R&D Projects: GA AV ČR IAA600930609 Institutional research plan: CEZ:AV0Z60930519 Keywords : chamois * ice ages * mtDNA * phylogeography * taxonomy Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.052, year: 2009
Czech Academy of Sciences Publication Activity Database
Hulva, P.; Fornůsková, Alena; Chudárková, A.; Evin, A.; Allegrini, B.; Benda, P.; Bryja, Josef
2010-01-01
Roč. 19, č. 24 (2010), s. 5417-5431 ISSN 0962-1083 R&D Projects: GA MŠk LC06073 Institutional research plan: CEZ:AV0Z60930519 Keywords : hybrid speciation * introgression * Mediterranean * microsatellites * mitochondrial DNA * phylogeography * bats * radiation Subject RIV: EG - Zoology Impact factor: 6.457, year: 2010
Czech Academy of Sciences Publication Activity Database
Lecocq, T.; Dellicour, S.; Michez, D.; Lhomme, P.; Vanderplanck, M.; Valterová, Irena; Rasplus, J. Y.; Rasmont, P.
2013-01-01
Roč. 13, č. 263 (2013), 263/1-263/17 ISSN 1471-2148 Grant - others:Seventh Framework Programme(XE) FP7-244090 Institutional support: RVO:61388963 Keywords : phylogeography * reproductive traits * genetic differentiation * bumblebees Subject RIV: CC - Organic Chemistry Impact factor: 3.407, year: 2013 http://www.biomedcentral.com/1471-2148/13/263
Bayesian seismic AVO inversion
Energy Technology Data Exchange (ETDEWEB)
Buland, Arild
2002-07-01
A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P-wave velocity, S-wave velocity and density. Distributions for other elastic parameters can also be assessed, for example acoustic impedance, shear impedance and P-wave to S-wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance, hence exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3-D dataset from the Sleipner Field. The results show good agreement with well logs but the uncertainty is high. The stochastic model includes uncertainties of both the elastic parameters, the wavelet and the seismic and well log data. The posterior distribution is explored by Markov chain Monte Carlo simulation using the Gibbs sampler algorithm. The inversion algorithm has been tested on a seismic line from the Heidrun Field with two wells located on the line. The uncertainty of the estimated wavelet is low. In the Heidrun examples the effect of including uncertainty of the wavelet and the noise level was marginal with respect to the AVO inversion results. We have developed a 3-D linearized AVO inversion method with spatially coupled model parameters where the objective is to obtain posterior distributions for P-wave velocity, S
Bayesian networks improve causal environmental ...
Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value
Bayesian Latent Class Analysis Tutorial.
Li, Yuelin; Lord-Bessen, Jennifer; Shiyko, Mariya; Loeb, Rebecca
2018-01-01
This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.
Kernel Bayesian ART and ARTMAP.
Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan
2018-02-01
Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.
Pabijan, Maciej; Brown, Jason L; Chan, Lauren M; Rakotondravony, Hery A; Raselimanana, Achille P; Yoder, Anne D; Glaw, Frank; Vences, Miguel
2015-11-01
The rainforest biome of eastern Madagascar is renowned for its extraordinary biodiversity and restricted distribution ranges of many species, whereas the arid western region of the island is relatively species poor. We provide insight into the biogeography of western Madagascar by analyzing a multilocus phylogeographic dataset assembled for an amphibian, the widespread Malagasy bullfrog, Laliostoma labrosum. We find no cryptic species in L. labrosum (maximum 1.1% pairwise genetic distance between individuals in the 16S rRNA gene) attributable to considerable gene flow at the regional level as shown by genetic admixture in both mtDNA and three nuclear loci, especially in central Madagascar. Low breeding site fidelity, viewed as an adaptation to the unreliability of standing pools of freshwater in dry and seasonal environments, and a ubiquitous distribution within its range may underlie overall low genetic differentiation. Moreover, reductions in population size associated with periods of high aridity in western Madagascar may have purged DNA variation in this species. The mtDNA gene tree revealed seven major phylogroups within this species, five of which show mostly non-overlapping distributions. The nested positions of the northern and central mtDNA phylogroups imply a southwestern origin for all extant mtDNA lineages in L. labrosum. The current phylogeography of this species and paleo-distributions of major mtDNA lineages suggest five potential refugia in northern, western and southwestern Madagascar, likely the result of Pleistocene range fragmentation during drier and cooler climates. Lineage sorting in mtDNA and nuclear loci highlighted a main phylogeographic break between populations north and south of the Sambirano region, suggesting a role of the coastal Sambirano rainforest as a barrier to gene flow. Paleo-species distribution models and dispersal networks suggest that the persistence of some refugial populations was mainly determined by high population
Directory of Open Access Journals (Sweden)
Daniele Porretta
Full Text Available BACKGROUND: The tiger mosquito, Aedes albopictus, is one of the 100 most invasive species in the world and a vector of human diseases. In the last 30 years, it has spread from its native range in East Asia to Africa, Europe, and the Americas. Although this modern invasion has been the focus of many studies, the history of the species' native populations remains poorly understood. Here, we aimed to assess the role of Pleistocene climatic changes in shaping the current distribution of the species in its native range. METHODOLOGY/PRINCIPAL FINDINGS: We investigated the phylogeography, historical demography, and species distribution of Ae. albopictus native populations at the Last Glacial Maximum (LGM. Individuals from 16 localities from East Asia were analyzed for sequence variation at two mitochondrial genes. No phylogeographic structure was observed across the study area. Demographic analyses showed a signature of population expansion that started roughly 70,000 years BP. The occurrence of a continuous and climatically suitable area comprising Southeast China, Indochinese Peninsula, and Sundaland during LGM was indicated by species distribution modelling. CONCLUSIONS/SIGNIFICANCE: Our results suggest an evolutionary scenario in which, during the last glacial phase, Ae. albopictus did not experience a fragmentation phase but rather persisted in interconnected populations and experienced demographic growth. The wide ecological flexibility of the species probably played a crucial role in its response to glacial-induced environmental changes. Currently, there is little information on the impact of Pleistocene climatic changes on animal species in East Asia. Most of the studies focused on forest-associated species and suggested cycles of glacial fragmentation and post-glacial expansion. The case of Ae. albopictus, which exhibits a pattern not previously observed in the study area, adds an important piece to our understanding of the Pleistocene history
Bayesian genomic selection: the effect of haplotype lenghts and priors
DEFF Research Database (Denmark)
Villumsen, Trine Michelle; Janss, Luc
2009-01-01
Breeding values for animals with marker data are estimated using a genomic selection approach where data is analyzed using Bayesian multi-marker association models. Fourteen model scenarios with varying haplotype lengths, hyper parameter and prior distributions were compared to find the scenario ...
The bootstrap and Bayesian bootstrap method in assessing bioequivalence
International Nuclear Information System (INIS)
Wan Jianping; Zhang Kongsheng; Chen Hui
2009-01-01
Parametric method for assessing individual bioequivalence (IBE) may concentrate on the hypothesis that the PK responses are normal. Nonparametric method for evaluating IBE would be bootstrap method. In 2001, the United States Food and Drug Administration (FDA) proposed a draft guidance. The purpose of this article is to evaluate the IBE between test drug and reference drug by bootstrap and Bayesian bootstrap method. We study the power of bootstrap test procedures and the parametric test procedures in FDA (2001). We find that the Bayesian bootstrap method is the most excellent.
Phylogeography of the current rabies viruses in Indonesia
Dibia, I Nyoman; Sumiarto, Bambang; Susetya, Heru; Putra, Anak Agung Gde; Scott-Orr, Helen
2015-01-01
Rabies is a major fatal zoonotic disease in Indonesia. This study was conducted to determine the recent dynamics of rabies virus (RABV) in various areas and animal species throughout Indonesia. A total of 27 brain samples collected from rabid animals of various species in Bali, Sumatra, Kalimantan, Sulawesi, Java, and Flores in 2008 to 2010 were investigated. The cDNA of the nucleoprotein gene from each sample was generated and amplified by one-step reverse transcription-PCR, after which the products were sequenced and analyzed. The symmetric substitution model of a Bayesian stochastic search variable selection extension of the discrete phylogeographic model of the social network was applied in BEAST ver. 1.7.5 software. The spatial dispersal was visualized in Cartographica using Spatial Phylogenetic Reconstruction of Evolutionary Dynamics. We demonstrated inter-island introduction and reintroduction, and dog was found to be the only source of infection of other animals. Ancestors of Indonesian RABVs originated in Java and its descendants were transmitted to Kalimantan, then further to Sumatra, Flores, and Bali. The Flores descendent was subsequently transmitted to Sulawesi and back to Kalimantan. The viruses found in various animal species were transmitted by the dog. PMID:25643792
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....
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.
Learning Bayesian networks for discrete data
Liang, Faming; Zhang, Jian
2009-01-01
Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly
Bayesian Network Induction via Local Neighborhoods
National Research Council Canada - National Science Library
Margaritis, Dimitris
1999-01-01
.... We present an efficient algorithm for learning Bayesian networks from data. Our approach constructs Bayesian networks by first identifying each node's Markov blankets, then connecting nodes in a consistent way...
Can a significance test be genuinely Bayesian?
Pereira, Carlos A. de B.; Stern, Julio Michael; Wechsler, Sergio
2008-01-01
The Full Bayesian Significance Test, FBST, is extensively reviewed. Its test statistic, a genuine Bayesian measure of evidence, is discussed in detail. Its behavior in some problems of statistical inference like testing for independence in contingency tables is discussed.
Bayesian modeling using WinBUGS
Ntzoufras, Ioannis
2009-01-01
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...
Inference in hybrid Bayesian networks
International Nuclear Information System (INIS)
Langseth, Helge; Nielsen, Thomas D.; Rumi, Rafael; Salmeron, Antonio
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 and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (the so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability.
3D Bayesian contextual classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
2000-01-01
We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....
Bayesian methods for proteomic biomarker development
Directory of Open Access Journals (Sweden)
Belinda Hernández
2015-12-01
In this review we provide an introduction to Bayesian inference and demonstrate some of the advantages of using a Bayesian framework. We summarize how Bayesian methods have been used previously in proteomics and other areas of bioinformatics. Finally, we describe some popular and emerging Bayesian models from the statistical literature and provide a worked tutorial including code snippets to show how these methods may be applied for the evaluation of proteomic biomarkers.
Phylogeography of mtDNA haplogroup R7 in the Indian peninsula
Directory of Open Access Journals (Sweden)
Shukla Parul
2008-08-01
Full Text Available Abstract Background Human genetic diversity observed in Indian subcontinent is second only to that of Africa. This implies an early settlement and demographic growth soon after the first 'Out-of-Africa' dispersal of anatomically modern humans in Late Pleistocene. In contrast to this perspective, linguistic diversity in India has been thought to derive from more recent population movements and episodes of contact. With the exception of Dravidian, which origin and relatedness to other language phyla is obscure, all the language families in India can be linked to language families spoken in different regions of Eurasia. Mitochondrial DNA and Y chromosome evidence has supported largely local evolution of the genetic lineages of the majority of Dravidian and Indo-European speaking populations, but there is no consensus yet on the question of whether the Munda (Austro-Asiatic speaking populations originated in India or derive from a relatively recent migration from further East. Results Here, we report the analysis of 35 novel complete mtDNA sequences from India which refine the structure of Indian-specific varieties of haplogroup R. Detailed analysis of haplogroup R7, coupled with a survey of ~12,000 mtDNAs from caste and tribal groups over the entire Indian subcontinent, reveals that one of its more recently derived branches (R7a1, is particularly frequent among Munda-speaking tribal groups. This branch is nested within diverse R7 lineages found among Dravidian and Indo-European speakers of India. We have inferred from this that a subset of Munda-speaking groups have acquired R7 relatively recently. Furthermore, we find that the distribution of R7a1 within the Munda-speakers is largely restricted to one of the sub-branches (Kherwari of northern Munda languages. This evidence does not support the hypothesis that the Austro-Asiatic speakers are the primary source of the R7 variation. Statistical analyses suggest a significant correlation between
Bayesian networks and food security - An introduction
Stein, A.
2004-01-01
This paper gives an introduction to Bayesian networks. Networks are defined and put into a Bayesian context. Directed acyclical graphs play a crucial role here. Two simple examples from food security are addressed. Possible uses of Bayesian networks for implementation and further use in decision
Plug & Play object oriented Bayesian networks
DEFF Research Database (Denmark)
Bangsø, Olav; Flores, J.; Jensen, Finn Verner
2003-01-01
been shown to be quite suitable for dynamic domains as well. However, processing object oriented Bayesian networks in practice does not take advantage of their modular structure. Normally the object oriented Bayesian network is transformed into a Bayesian network and, inference is performed...... dynamic domains. The communication needed between instances is achieved by means of a fill-in propagation scheme....
A Bayesian framework for risk perception
van Erp, H.R.N.
2017-01-01
We present here a Bayesian framework of risk perception. This framework encompasses plausibility judgments, decision making, and question asking. Plausibility judgments are modeled by way of Bayesian probability theory, decision making is modeled by way of a Bayesian decision theory, and relevancy
Phylogeography of Japanese encephalitis virus: genotype is associated with climate.
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Amy J Schuh
Full Text Available The circulation of vector-borne zoonotic viruses is largely determined by the overlap in the geographical distributions of virus-competent vectors and reservoir hosts. What is less clear are the factors influencing the distribution of virus-specific lineages. Japanese encephalitis virus (JEV is the most important etiologic agent of epidemic encephalitis worldwide, and is primarily maintained between vertebrate reservoir hosts (avian and swine and culicine mosquitoes. There are five genotypes of JEV: GI-V. In recent years, GI has displaced GIII as the dominant JEV genotype and GV has re-emerged after almost 60 years of undetected virus circulation. JEV is found throughout most of Asia, extending from maritime Siberia in the north to Australia in the south, and as far as Pakistan to the west and Saipan to the east. Transmission of JEV in temperate zones is epidemic with the majority of cases occurring in summer months, while transmission in tropical zones is endemic and occurs year-round at lower rates. To test the hypothesis that viruses circulating in these two geographical zones are genetically distinct, we applied Bayesian phylogeographic, categorical data analysis and phylogeny-trait association test techniques to the largest JEV dataset compiled to date, representing the envelope (E gene of 487 isolates collected from 12 countries over 75 years. We demonstrated that GIII and the recently emerged GI-b are temperate genotypes likely maintained year-round in northern latitudes, while GI-a and GII are tropical genotypes likely maintained primarily through mosquito-avian and mosquito-swine transmission cycles. This study represents a new paradigm directly linking viral molecular evolution and climate.
Spectral analysis of the IntCal98 calibration curve: a Bayesian view
International Nuclear Information System (INIS)
Palonen, V.; Tikkanen, P.
2004-01-01
Preliminary results from a Bayesian approach to find periodicities in the IntCal98 calibration curve are given. It has been shown in the literature that the discrete Fourier transform (Schuster periodogram) corresponds to the use of an approximate Bayesian model of one harmonic frequency and Gaussian noise. Advantages of the Bayesian approach include the possibility to use models for variable, attenuated and multiple frequencies, the capability to analyze unevenly spaced data and the possibility to assess the significance and uncertainties of spectral estimates. In this work, a new Bayesian model using random walk noise to take care of the trend in the data is developed. Both Bayesian models are described and the first results of the new model are reported and compared with results from straightforward discrete-Fourier-transform and maximum-entropy-method spectral analyses
BATSE gamma-ray burst line search. 2: Bayesian consistency methodology
Band, D. L.; Ford, L. A.; Matteson, J. L.; Briggs, M.; Paciesas, W.; Pendleton, G.; Preece, R.; Palmer, D.; Teegarden, B.; Schaefer, B.
1994-01-01
We describe a Bayesian methodology to evaluate the consistency between the reported Ginga and Burst and Transient Source Experiment (BATSE) detections of absorption features in gamma-ray burst spectra. Currently no features have been detected by BATSE, but this methodology will still be applicable if and when such features are discovered. The Bayesian methodology permits the comparison of hypotheses regarding the two detectors' observations and makes explicit the subjective aspects of our analysis (e.g., the quantification of our confidence in detector performance). We also present non-Bayesian consistency statistics. Based on preliminary calculations of line detectability, we find that both the Bayesian and non-Bayesian techniques show that the BATSE and Ginga observations are consistent given our understanding of these detectors.
Bayesian NL interpretation and learning
Zeevat, H.
2011-01-01
Everyday natural language communication is normally successful, even though contemporary computational linguistics has shown that NL is characterised by very high degree of ambiguity and the results of stochastic methods are not good enough to explain the high success rate. Bayesian natural language
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis
configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed...
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis Linda
2006-01-01
configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for the salt and pepper noise. The inference in the model is discussed...
Differentiated Bayesian Conjoint Choice Designs
Z. Sándor (Zsolt); M. Wedel (Michel)
2003-01-01
textabstractPrevious conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new...
Bayesian Sampling using Condition Indicators
DEFF Research Database (Denmark)
Faber, Michael H.; Sørensen, John Dalsgaard
2002-01-01
of condition indicators introduced by Benjamin and Cornell (1970) a Bayesian approach to quality control is formulated. The formulation is then extended to the case where the quality control is based on sampling of indirect information about the condition of the components, i.e. condition indicators...
Bayesian Classification of Image Structures
DEFF Research Database (Denmark)
Goswami, Dibyendu; Kalkan, Sinan; Krüger, Norbert
2009-01-01
In this paper, we describe work on Bayesian classi ers for distinguishing between homogeneous structures, textures, edges and junctions. We build semi-local classiers from hand-labeled images to distinguish between these four different kinds of structures based on the concept of intrinsic dimensi...
Bayesian estimates of linkage disequilibrium
Directory of Open Access Journals (Sweden)
Abad-Grau María M
2007-06-01
Full Text Available Abstract Background The maximum likelihood estimator of D' – a standard measure of linkage disequilibrium – is biased toward disequilibrium, and the bias is particularly evident in small samples and rare haplotypes. Results This paper proposes a Bayesian estimation of D' to address this problem. The reduction of the bias is achieved by using a prior distribution on the pair-wise associations between single nucleotide polymorphisms (SNPs that increases the likelihood of equilibrium with increasing physical distances between pairs of SNPs. We show how to compute the Bayesian estimate using a stochastic estimation based on MCMC methods, and also propose a numerical approximation to the Bayesian estimates that can be used to estimate patterns of LD in large datasets of SNPs. Conclusion Our Bayesian estimator of D' corrects the bias toward disequilibrium that affects the maximum likelihood estimator. A consequence of this feature is a more objective view about the extent of linkage disequilibrium in the human genome, and a more realistic number of tagging SNPs to fully exploit the power of genome wide association studies.
3-D contextual Bayesian classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
In this paper we will consider extensions of a series of Bayesian 2-D contextual classification pocedures proposed by Owen (1984) Hjort & Mohn (1984) and Welch & Salter (1971) and Haslett (1985) to 3 spatial dimensions. It is evident that compared to classical pixelwise classification further...
Learning Bayesian Dependence Model for Student Modelling
Directory of Open Access Journals (Sweden)
Adina COCU
2008-12-01
Full Text Available Learning a Bayesian network from a numeric set of data is a challenging task because of dual nature of learning process: initial need to learn network structure, and then to find out the distribution probability tables. In this paper, we propose a machine-learning algorithm based on hill climbing search combined with Tabu list. The aim of learning process is to discover the best network that represents dependences between nodes. Another issue in machine learning procedure is handling numeric attributes. In order to do that, we must perform an attribute discretization pre-processes. This discretization operation can influence the results of learning network structure. Therefore, we make a comparative study to find out the most suitable combination between discretization method and learning algorithm, for a specific data set.
Bayesian Alternation During Tactile Augmentation
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Caspar Mathias Goeke
2016-10-01
Full Text Available A large number of studies suggest that the integration of multisensory signals by humans is well described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition, rotation only (native condition, and both augmented and native information (bimodal condition. Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants’ responses with a probit model and calculated the just notable difference (JND. Then we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χred2 = 1.67 than the Bayesian integration model (χred2= 4.34. Slightly higher accuracy showed a non-Bayesian winner takes all model (χred2= 1.64, which either used only native or only augmented values per subject for prediction. However the performance of the Bayesian alternation model could be substantially improved (χred2= 1.09 utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in
Topics in Bayesian statistics and maximum entropy
International Nuclear Information System (INIS)
Mutihac, R.; Cicuttin, A.; Cerdeira, A.; Stanciulescu, C.
1998-12-01
Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling. The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information, and Bayesian court decision-making. (author)
BAYESIAN MAGNETOHYDRODYNAMIC SEISMOLOGY OF CORONAL LOOPS
International Nuclear Information System (INIS)
Arregui, I.; Asensio Ramos, A.
2011-01-01
We perform a Bayesian parameter inference in the context of resonantly damped transverse coronal loop oscillations. The forward problem is solved in terms of parametric results for kink waves in one-dimensional flux tubes in the thin tube and thin boundary approximations. For the inverse problem, we adopt a Bayesian approach to infer the most probable values of the relevant parameters, for given observed periods and damping times, and to extract their confidence levels. The posterior probability distribution functions are obtained by means of Markov Chain Monte Carlo simulations, incorporating observed uncertainties in a consistent manner. We find well-localized solutions in the posterior probability distribution functions for two of the three parameters of interest, namely the Alfven travel time and the transverse inhomogeneity length scale. The obtained estimates for the Alfven travel time are consistent with previous inversion results, but the method enables us to additionally constrain the transverse inhomogeneity length scale and to estimate real error bars for each parameter. When observational estimates for the density contrast are used, the method enables us to fully constrain the three parameters of interest. These results can serve to improve our current estimates of unknown physical parameters in coronal loops and to test the assumed theoretical model.
The Bayesian Approach to Association
Arora, N. S.
2017-12-01
The Bayesian approach to Association focuses mainly on quantifying the physics of the domain. In the case of seismic association for instance let X be the set of all significant events (above some threshold) and their attributes, such as location, time, and magnitude, Y1 be the set of detections that are caused by significant events and their attributes such as seismic phase, arrival time, amplitude etc., Y2 be the set of detections that are not caused by significant events, and finally Y be the set of observed detections We would now define the joint distribution P(X, Y1, Y2, Y) = P(X) P(Y1 | X) P(Y2) I(Y = Y1 + Y2) ; where the last term simply states that Y1 and Y2 are a partitioning of Y. Given the above joint distribution the inference problem is simply to find the X, Y1, and Y2 that maximizes posterior probability P(X, Y1, Y2| Y) which reduces to maximizing P(X) P(Y1 | X) P(Y2) I(Y = Y1 + Y2). In this expression P(X) captures our prior belief about event locations. P(Y1 | X) captures notions of travel time, residual error distributions as well as detection and mis-detection probabilities. While P(Y2) captures the false detection rate of our seismic network. The elegance of this approach is that all of the assumptions are stated clearly in the model for P(X), P(Y1|X) and P(Y2). The implementation of the inference is merely a by-product of this model. In contrast some of the other methods such as GA hide a number of assumptions in the implementation details of the inference - such as the so called "driver cells." The other important aspect of this approach is that all seismic knowledge including knowledge from other domains such as infrasound and hydroacoustic can be included in the same model. So, we don't need to separately account for misdetections or merge seismic and infrasound events as a separate step. Finally, it should be noted that the objective of automatic association is to simplify the job of humans who are publishing seismic bulletins based on this
Directory of Open Access Journals (Sweden)
Tania Anaid Gutiérrez-García
2013-12-01
Full Text Available Central America is an ideal region for comparative phylogeographic studies because of its intricate geologic and biogeographic history, diversity of habitats and dynamic climatic and tectonic history. The aim of this work was to assess the phylogeography of two rodents codistributed throughout Central America, in order to identify if they show concordant genetic and phylogeographic patterns. The synopsis includes four parts: (1 an overview of the field of comparative phylogeography; (2 a detailed review that describes how genetic and geologic studies can be combined to elucidate general patterns of the biogeographic and evolutionary history of Central America; and a phylogeographic analysis of two species at both the (3 intraspecific and (4 comparative phylogeographic levels. The last incorporates specific ecological features and evaluates their influence on the species’ genetic patterns. Results showed a concordant genetic structure influenced by geographic distance for both rodents, but dissimilar dispersal patterns due to ecological features and life history.
Wang, Mei-Na; Duan, Lei; Qiao, Qi; Wang, Zheng-Feng; Zimmer, Elizabeth A; Li, Zhong-Chao; Chen, Hong-Feng
2018-01-01
Bretschneidera sinensis, a class-I protected wild plant in China, is a relic of the ancient Tertiary tropical flora endemic to Asia. However, little is known about its genetics and phylogeography. To elucidate the current phylogeographic patterns and infer the historical population dynamics of B. sinensis, and to make recommendations for its conservation, three non-coding regions of chloroplast DNA (trnQ-rps16, rps8-rps11, and trnT-trnL) were amplified and sequenced across 256 individuals from 23 populations of B. sinensis, spanning 10 provinces of China. We recognized 13 haplotypes, demonstrating relatively high total haplotype diversity (hT = 0.739). Almost all of the variation existed among populations (98.09%, P units.
Yang, Bao-Tian; Zhou, Yu; Min, Mi-Sook; Matsui, Masafumi; Dong, Bing-Jun; Li, Pi-Peng; Fong, Jonathan J
2017-07-01
We investigated the species diversity and phylogeography of the Northeast Asian brown frogs allied to Rana dybowskii (the R. dybowskii species complex: R. dybowskii, R. pirica, and R. uenoi) using four mitochondrial and three nuclear loci. Phylogenetic analyses confirmed the existence of three distinct species in this complex; using extensive molecular data, we confirm the validity of Rana uenoi recognized as a distinct species, and infer R. dybowskii and R. pirica to be sister species. Also, we included populations from previously unsampled regions in Northeast China, and identified them to be R. dybowskii. While many species in Northeast Asia diverged due to Pleistocene glaciation, divergence-dating analyses inferred older, Miocene speciation in the R. dybowskii species complex. Ancestral area reconstruction identified the orogenic movement of the Changbai Mountain Range and the opening of the Sea of Japan/East Sea being major events influencing allopatric speciation. Copyright © 2017 Elsevier Inc. All rights reserved.
Bayesian estimation methods in metrology
International Nuclear Information System (INIS)
Cox, M.G.; Forbes, A.B.; Harris, P.M.
2004-01-01
In metrology -- the science of measurement -- a measurement result must be accompanied by a statement of its associated uncertainty. The degree of validity of a measurement result is determined by the validity of the uncertainty statement. In recognition of the importance of uncertainty evaluation, the International Standardization Organization in 1995 published the Guide to the Expression of Uncertainty in Measurement and the Guide has been widely adopted. The validity of uncertainty statements is tested in interlaboratory comparisons in which an artefact is measured by a number of laboratories and their measurement results compared. Since the introduction of the Mutual Recognition Arrangement, key comparisons are being undertaken to determine the degree of equivalence of laboratories for particular measurement tasks. In this paper, we discuss the possible development of the Guide to reflect Bayesian approaches and the evaluation of key comparison data using Bayesian estimation methods
Deep Learning and Bayesian Methods
Directory of Open Access Journals (Sweden)
Prosper Harrison B.
2017-01-01
Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Bayesian inference on proportional elections.
Directory of Open Access Journals (Sweden)
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.
BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS
Directory of Open Access Journals (Sweden)
Thordis Linda Thorarinsdottir
2011-05-01
Full Text Available In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.
Bayesian Networks as a Decision Tool for O&M of Offshore Wind Turbines
DEFF Research Database (Denmark)
Nielsen, Jannie Jessen; Sørensen, John Dalsgaard
2010-01-01
Costs to operation and maintenance (O&M) of offshore wind turbines are large. This paper presents how influence diagrams can be used to assist in rational decision making for O&M. An influence diagram is a graphical representation of a decision tree based on Bayesian Networks. Bayesian Networks...... offer efficient Bayesian updating of a damage model when imperfect information from inspections/monitoring is available. The extension to an influence diagram offers the calculation of expected utilities for decision alternatives, and can be used to find the optimal strategy among different alternatives...
Bayesian naturalness, simplicity, and testability applied to the B ‑ L MSSM GUT
Fundira, Panashe; Purves, Austin
2018-04-01
Recent years have seen increased use of Bayesian model comparison to quantify notions such as naturalness, simplicity, and testability, especially in the area of supersymmetric model building. After demonstrating that Bayesian model comparison can resolve a paradox that has been raised in the literature concerning the naturalness of the proton mass, we apply Bayesian model comparison to GUTs, an area to which it has not been applied before. We find that the GUTs are substantially favored over the nonunifying puzzle model. Of the GUTs we consider, the B ‑ L MSSM GUT is the most favored, but the MSSM GUT is almost equally favored.
Phylogeography, Genetic Diversity, and Management Units of Hawksbill Turtles in the Indo-Pacific.
Vargas, Sarah M; Jensen, Michael P; Ho, Simon Y W; Mobaraki, Asghar; Broderick, Damien; Mortimer, Jeanne A; Whiting, Scott D; Miller, Jeff; Prince, Robert I T; Bell, Ian P; Hoenner, Xavier; Limpus, Colin J; Santos, Fabrício R; FitzSimmons, Nancy N
2016-05-01
Hawksbill turtle (Eretmochelys imbricata) populations have experienced global decline because of a history of intense commercial exploitation for shell and stuffed taxidermied whole animals, and harvest for eggs and meat. Improved understanding of genetic diversity and phylogeography is needed to aid conservation. In this study, we analyzed the most geographically comprehensive sample of hawksbill turtles from the Indo-Pacific Ocean, sequencing 766 bp of the mitochondrial control region from 13 locations (plus Aldabra, n = 4) spanning over 13500 km. Our analysis of 492 samples revealed 52 haplotypes distributed in 5 divergent clades. Diversification times differed between the Indo-Pacific and Atlantic lineages and appear to be related to the sea-level changes that occurred during the Last Glacial Maximum. We found signals of demographic expansion only for turtles from the Persian Gulf region, which can be tied to a more recent colonization event. Our analyses revealed evidence of transoceanic migration, including connections between feeding grounds from the Atlantic Ocean and Indo-Pacific rookeries. Hawksbill turtles appear to have a complex pattern of phylogeography, showing a weak isolation by distance and evidence of multiple colonization events. Our novel dataset will allow mixed-stock analyses of hawksbill turtle feeding grounds in the Indo-Pacific by providing baseline data needed for conservation efforts in the region. Eight management units are proposed in our study for the Indo-Pacific region that can be incorporated in conservation plans of this critically endangered species. © The American Genetic Association. 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Space Shuttle RTOS Bayesian Network
Morris, A. Terry; Beling, Peter A.
2001-01-01
With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores
Multiview Bayesian Correlated Component Analysis
DEFF Research Database (Denmark)
Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai
2015-01-01
are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....
Safety culture in Bayesian and legal contexts
International Nuclear Information System (INIS)
Krug, H.E.P. Jr.
1992-01-01
While contemplating the similarities between the law of torts and concepts of safety, the author realized that there was a close correspondence between the law of negligence and the way safety ought to be generally defined. This definition of safety is provided herein. A safety culture must have an adequate definition of safety in order to function most effectively. This paper provides a practical definition of safety that answers the question 'How safe is safe enough? The development rests on two bases: the subjectivistic-Bayesian definition of probability and certain legal definitions primarily from the tort law of negligence. The development also leads to the conclusion that one cannot generally expect greater specificity in determining how safe is safe enough than one finds in the legal definition of liability under the tort of negligence. It then follows that some of the public's aversion to complex technical undertakings is rooted in its typically intuitive and vague notions concerning safety
12th Brazilian Meeting on Bayesian Statistics
Louzada, Francisco; Rifo, Laura; Stern, Julio; Lauretto, Marcelo
2015-01-01
Through refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesia...
Hassanin, Alexandre; Khouider, Souraya; Gembu, Guy-Crispin; Goodman, Steven M; Kadjo, Blaise; Nesi, Nicolas; Pourrut, Xavier; Nakouné, Emmanuel; Bonillo, Céline
2015-03-01
The hypothesis of Pleistocene forest refugia was tested using comparative phylogeography of Scotonycterini, a fruit bat tribe endemic to Africa containing four species: Scotonycteris zenkeri, Casinycteris argynnis, C. campomaanensis, and C. ophiodon. Patterns of genetic structure were assessed using 105 Scotonycterini (including material from three holotypes) collected at 37 localities, and DNA sequences from the mitochondrial cytochrome b gene (1140 nt) and 12 nuclear introns (9641 nt). Phylogenetic trees and molecular dating were inferred by Bayesian methods. Multilocus analyses were performed using supermatrix, SuperTRI, and *BEAST approaches. Mitochondrial analyses reveal strong phylogeographical structure in Scotonycteris, with four divergent haplogroups (4.9-8.7%), from Upper Guinea, Cameroon, western Equatorial Africa, and eastern Democratic Republic of the Congo (DRC). In C. argynnis, we identify two mtDNA haplogroups corresponding to western and eastern Equatorial Africa (1.4-2.1%). In C. ophiodon, the mtDNA haplotypes from Cameroon and Ivory Coast differ by only 1.3%. Nuclear analyses confirm the validity of the recently described C. campomaanensis and indicate that western and eastern populations of C. argynnis are not fully isolated. All mtDNA clusters detected in Scotonycteris are found to be monophyletic based on the nuclear dataset, except in eastern DRC. In the nuclear tree, the clade from western Equatorial Africa is closely related to individuals from eastern DRC, whereas in the mitochondrial tree it appears to be the sister-group of the Cameroon clade. Migrate-n analyses support gene flow from western Equatorial Africa to eastern DRC. Molecular dating indicates that Pleistocene forest refugia have played an important role in shaping the evolution of Scotonycterini, with two phases of allopatric speciation at approximately 2.7 and 1.6 Mya, resulting from isolation in three main forest areas corresponding to Upper Guinea, Cameroon, and Equatorial
A Bayesian model for binary Markov chains
Directory of Open Access Journals (Sweden)
Belkheir Essebbar
2004-02-01
Full Text Available This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.
Directory of Open Access Journals (Sweden)
Khurram Shahzad
2017-11-01
profoundly shaped the population genetic divergence and demographic dynamics of Notopterygium species. The findings of this and previous studies provide important insights into the effects of QTP uplifts and climatic changes on phylogeography and species differentiation in high altitude mountainous areas. Our results may also facilitate the conservation of endangered herbaceous medicinal plants in the genus Notopterygium.
3rd Bayesian Young Statisticians Meeting
Lanzarone, Ettore; Villalobos, Isadora; Mattei, Alessandra
2017-01-01
This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).
Nonadditive entropy maximization is inconsistent with Bayesian updating
Pressé, Steve
2014-11-01
The maximum entropy method—used to infer probabilistic models from data—is a special case of Bayes's model inference prescription which, in turn, is grounded in basic propositional logic. By contrast to the maximum entropy method, the compatibility of nonadditive entropy maximization with Bayes's model inference prescription has never been established. Here we demonstrate that nonadditive entropy maximization is incompatible with Bayesian updating and discuss the immediate implications of this finding. We focus our attention on special cases as illustrations.
A Decomposition Algorithm for Learning Bayesian Network Structures from Data
DEFF Research Database (Denmark)
Zeng, Yifeng; Cordero Hernandez, Jorge
2008-01-01
It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....
Bayesian networks for evaluation of evidence from forensic entomology.
Andersson, M Gunnar; Sundström, Anders; Lindström, Anders
2013-09-01
In the aftermath of a CBRN incident, there is an urgent need to reconstruct events in order to bring the perpetrators to court and to take preventive actions for the future. The challenge is to discriminate, based on available information, between alternative scenarios. Forensic interpretation is used to evaluate to what extent results from the forensic investigation favor the prosecutors' or the defendants' arguments, using the framework of Bayesian hypothesis testing. Recently, several new scientific disciplines have been used in a forensic context. In the AniBioThreat project, the framework was applied to veterinary forensic pathology, tracing of pathogenic microorganisms, and forensic entomology. Forensic entomology is an important tool for estimating the postmortem interval in, for example, homicide investigations as a complement to more traditional methods. In this article we demonstrate the applicability of the Bayesian framework for evaluating entomological evidence in a forensic investigation through the analysis of a hypothetical scenario involving suspect movement of carcasses from a clandestine laboratory. Probabilities of different findings under the alternative hypotheses were estimated using a combination of statistical analysis of data, expert knowledge, and simulation, and entomological findings are used to update the beliefs about the prosecutors' and defendants' hypotheses and to calculate the value of evidence. The Bayesian framework proved useful for evaluating complex hypotheses using findings from several insect species, accounting for uncertainty about development rate, temperature, and precolonization. The applicability of the forensic statistic approach to evaluating forensic results from a CBRN incident is discussed.
Bayesian phylogenetic estimation of fossil ages.
Drummond, Alexei J; Stadler, Tanja
2016-07-19
Recent advances have allowed for both morphological fossil evidence and molecular sequences to be integrated into a single combined inference of divergence dates under the rule of Bayesian probability. In particular, the fossilized birth-death tree prior and the Lewis-Mk model of discrete morphological evolution allow for the estimation of both divergence times and phylogenetic relationships between fossil and extant taxa. We exploit this statistical framework to investigate the internal consistency of these models by producing phylogenetic estimates of the age of each fossil in turn, within two rich and well-characterized datasets of fossil and extant species (penguins and canids). We find that the estimation accuracy of fossil ages is generally high with credible intervals seldom excluding the true age and median relative error in the two datasets of 5.7% and 13.2%, respectively. The median relative standard error (RSD) was 9.2% and 7.2%, respectively, suggesting good precision, although with some outliers. In fact, in the two datasets we analyse, the phylogenetic estimate of fossil age is on average less than 2 Myr from the mid-point age of the geological strata from which it was excavated. The high level of internal consistency found in our analyses suggests that the Bayesian statistical model employed is an adequate fit for both the geological and morphological data, and provides evidence from real data that the framework used can accurately model the evolution of discrete morphological traits coded from fossil and extant taxa. We anticipate that this approach will have diverse applications beyond divergence time dating, including dating fossils that are temporally unconstrained, testing of the 'morphological clock', and for uncovering potential model misspecification and/or data errors when controversial phylogenetic hypotheses are obtained based on combined divergence dating analyses.This article is part of the themed issue 'Dating species divergences using
Bayesian Methods and Universal Darwinism
Campbell, John
2009-12-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent Champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a `copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the Operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that Systems will evolve to states of highest entropy subject to the constraints of scientific law. This principle may be inverted to provide illumination as to the nature of scientific law. Our best cosmological theories suggest the universe contained much less complexity during the period shortly after the Big Bang than it does at present. The scientific subject matter of atomic physics, chemistry, biology and the social sciences has been created since that time. An explanation is proposed for the existence of this subject matter as due to the evolution of constraints in the form of adaptations imposed on Maximum Entropy. It is argued these adaptations were discovered and instantiated through the Operations of a succession of Darwinian processes.
Bayesian flood forecasting methods: A review
Han, Shasha; Coulibaly, Paulin
2017-08-01
Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
2013-01-01
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Range Wide Phylogeography of Dactylopius coccus (Hemiptera: Dactylopiidae)
DEFF Research Database (Denmark)
Van Dam, Alex; Portillo Martinez, Liberato; Jeri Chavez, Antonio
2015-01-01
The process of domestication and geographic origins of the cochineal insect (Dactylopius coccus Costa) has remained largely unstudied despite its importance as a global food colorant commodity. Ecological evidence supports Oaxaca Mexico as the geographic origin of this species. Other recent genet...... cochineal distributions. We find the center of origin of D. coccus to be Oaxaca Mexico based on mtDNA data and climate niche modeling. Further meta-genomic data are needed to rule out selective sweeps from past and present endosymbionts for these results to be definitive.......The process of domestication and geographic origins of the cochineal insect (Dactylopius coccus Costa) has remained largely unstudied despite its importance as a global food colorant commodity. Ecological evidence supports Oaxaca Mexico as the geographic origin of this species. Other recent genetic...... studies have been inconclusive. Here, we fill in the remaining gaps in the ecological record and look for corroboration from mtDNA markers as to the origin of this species. We use three mtDNA genes (CO1, tRNA-Leucine, and CO2) spanning 1294 bp, along with climate niche modeling of Holocene and Pleistocene...
Mitochondrial DNA phylogeography of least cisco Coregonus sardinella in Alaska.
Padula, V M; Causey, D; López, J A
2017-03-01
This study presents the first detailed analysis of the mitochondrial DNA diversity of least cisco Coregonus sardinella in Alaska using a 678 bp segment of the control region (D-loop) of the mitochondrial genome. Findings suggest that the history of C. sardinella in Alaska differs from that of other species of Coregonus present in the state and surrounding regions. The examined populations of C. sardinella are genetically diverse across Alaska. Sixty-eight distinct mitochondrial haplotypes were identified among 305 individuals sampled from nine locations. The haplotype minimum spanning network and phylogeny showed a modest level of geographic segregation among haplotypes, suggesting high levels of on-going or recent connectivity among distant populations. Observed Φ ST values and the results of homogeneity and AMOVAs indicate incipient genetic differentiation between aggregations in three broad regional groups. Sites north of the Brooks Range formed one group, sites in the Yukon and Selawik Rivers formed a second group and sites south of the Yukon drainage formed the third group. Overall, the sequence data showed that a large proportion of mtDNA genetic variation in C. sardinella is shared across Alaska, but this variation is not homogeneously distributed across all regions and for all haplotype groups. © 2017 The Fisheries Society of the British Isles.
Bayesian analysis of magnetic island dynamics
International Nuclear Information System (INIS)
Preuss, R.; Maraschek, M.; Zohm, H.; Dose, V.
2003-01-01
We examine a first order differential equation with respect to time used to describe magnetic islands in magnetically confined plasmas. The free parameters of this equation are obtained by employing Bayesian probability theory. Additionally, a typical Bayesian change point is solved in the process of obtaining the data
Learning dynamic Bayesian networks with mixed variables
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...
Using Bayesian Networks to Improve Knowledge Assessment
Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra
2013-01-01
In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…
Using Bayesian belief networks in adaptive management.
J.B. Nyberg; B.G. Marcot; R. Sulyma
2006-01-01
Bayesian belief and decision networks are relatively new modeling methods that are especially well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes for practioners of adaptive management, from illustrating system relations conceptually to...
Bayesian Decision Theoretical Framework for Clustering
Chen, Mo
2011-01-01
In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…
Robust Bayesian detection of unmodelled bursts
International Nuclear Information System (INIS)
Searle, Antony C; Sutton, Patrick J; Tinto, Massimo; Woan, Graham
2008-01-01
We develop a Bayesian treatment of the problem of detecting unmodelled gravitational wave bursts using the new global network of interferometric detectors. We also compare this Bayesian treatment with existing coherent methods, and demonstrate that the existing methods make implicit assumptions on the distribution of signals that make them sub-optimal for realistic signal populations
Bayesian models: A statistical primer for ecologists
Hobbs, N. Thompson; Hooten, Mevin B.
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models
Particle identification in ALICE: a Bayesian approach
Adam, J.; Adamova, D.; Aggarwal, M. M.; Rinella, G. Aglieri; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anticic, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshaeuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badala, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnafoeldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Bathen, B.; Batigne, G.; Camejo, A. Batista; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Benacek, P.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielcik, J.; Bielcikova, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Boggild, H.; Boldizsar, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossu, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Diaz, L. Calero; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castellanos, J. Castillo; Castro, A. J.; Casula, E. A. R.; Sanchez, C. Ceballos; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Barroso, V. Chibante; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Balbastre, G. Conesa; del Valle, Z. Conesa; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Morales, Y. Corrales; Cortes Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Denes, E.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Corchero, M. A. Diaz; Dietel, T.; Dillenseger, P.; Divia, R.; Djuvsland, O.; Dobrin, A.; Gimenez, D. Domenicis; Doenigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernandez Tellez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Girard, M. Fusco; Gaardhoje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, A.; Gheata, M.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glaessel, P.; Gomez Coral, D. M.; Ramirez, A. Gomez; Gonzalez, A. S.; Gonzalez, V.; Gonzalez-Zamora, P.; Gorbunov, S.; Goerlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Haake, R.; Haaland, O.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbaer, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacazio, N.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Bustamante, R. T. Jimenez; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kaplin, V.; Kar, S.; Uysal, A. Karasu; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Khan, M. Mohisin; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, J. S.; Kim, M.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein-Boesing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kostarakis, P.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Meethaleveedu, G. Koyithatta; Kralik, I.; Kravcakova, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kucera, V.; Kuijer, P. G.; Kumar, J.; Kumar, L.; Kumar, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; Monzon, I. Leon; Leon Vargas, H.; Leoncino, M.; Levai, P.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; Torres, E. Lopez; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mares, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marin, A.; Markert, C.; Marquard, M.; Martin, N. A.; Blanco, J. Martin; Martinengo, P.; Martinez, M. I.; Garcia, G. Martinez; Pedreira, M. Martinez; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Perez, J. Mercado; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montano Zetina, L.; Montes, E.; De Godoy, D. A. Moreira; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Muehlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paic, G.; Pal, S. K.; Pan, J.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Da Costa, H. Pereira; Peresunko, D.; Lara, C. E. Perez; Lezama, E. Perez; Peskov, V.; Pestov, Y.; Petracek, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Raesaenen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodriguez Cahuantzi, M.; Manso, A. Rodriguez; Roed, K.; Rogochaya, E.; Rohr, D.; Roehrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Montero, A. J. Rubio; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Safarik, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sandor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Sefcik, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shahzad, M. I.; Shangaraev, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; de Souza, R. D.; Sozzi, F.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Sumbera, M.; Sumowidagdo, S.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Munoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thaeder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Palomo, L. Valencia; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vyvre, P. Vande; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Vercellin, E.; Vergara Limon, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Baillie, O. Villalobos; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Voelkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrlakova, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Watanabe, D.; Watanabe, Y.; Weiser, D. F.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yang, H.; Yano, S.; Yasin, Z.; Yokoyama, H.; Yoo, I. -K.; Yoon, J. H.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Zavada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, C.; Zhao, C.; Zhigareva, N.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.
2016-01-01
We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian
Advances in Bayesian Modeling in Educational Research
Levy, Roy
2016-01-01
In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…
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...
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...
BELM: Bayesian extreme learning machine.
Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J
2011-03-01
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.
BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION.
Khakabimamaghani, Sahand; Ester, Martin
2016-01-01
The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification. Third, as most of the proposed stratification methods are deterministic, there is a need for investigating the potential benefits of applying probabilistic methods. To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. Our experimental results demonstrate the superiority of B2PS over a popular state-of-the-art method and the benefits of Bayesian approaches. Our results agree with the intuition that transcriptomic data forms a better basis for patient stratification than genomic data.
Bayesian Nonparametric Longitudinal Data Analysis.
Quintana, Fernando A; Johnson, Wesley O; Waetjen, Elaine; Gold, Ellen
2016-01-01
Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.
Microreact: visualizing and sharing data for genomic epidemiology and phylogeography.
Argimón, Silvia; Abudahab, Khalil; Goater, Richard J E; Fedosejev, Artemij; Bhai, Jyothish; Glasner, Corinna; Feil, Edward J; Holden, Matthew T G; Yeats, Corin A; Grundmann, Hajo; Spratt, Brian G; Aanensen, David M
2016-11-01
Visualization is frequently used to aid our interpretation of complex datasets. Within microbial genomics, visualizing the relationships between multiple genomes as a tree provides a framework onto which associated data (geographical, temporal, phenotypic and epidemiological) are added to generate hypotheses and to explore the dynamics of the system under investigation. Selected static images are then used within publications to highlight the key findings to a wider audience. However, these images are a very inadequate way of exploring and interpreting the richness of the data. There is, therefore, a need for flexible, interactive software that presents the population genomic outputs and associated data in a user-friendly manner for a wide range of end users, from trained bioinformaticians to front-line epidemiologists and health workers. Here, we present Microreact, a web application for the easy visualization of datasets consisting of any combination of trees, geographical, temporal and associated metadata. Data files can be uploaded to Microreact directly via the web browser or by linking to their location (e.g. from Google Drive/Dropbox or via API), and an integrated visualization via trees, maps, timelines and tables provides interactive querying of the data. The visualization can be shared as a permanent web link among collaborators, or embedded within publications to enable readers to explore and download the data. Microreact can act as an end point for any tool or bioinformatic pipeline that ultimately generates a tree, and provides a simple, yet powerful, visualization method that will aid research and discovery and the open sharing of datasets.
2nd Bayesian Young Statisticians Meeting
Bitto, Angela; Kastner, Gregor; Posekany, Alexandra
2015-01-01
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session ...
Molecular phylogeography of the Andean alpine plant, Gunnera magellanica
Shimizu, M.; Fujii, N.; Ito, M.; Asakawa, T.; Nishida, H.; Suyama, C.; Ueda, K.
2015-12-01
To clarify the evolutionary history of Gunnera magellanica (Gunneraceae), an alpine plant of the Andes mountains, we performed molecular phylogeographic analyses based on the sequences of an internal transcribed spacer (ITS) of nuclear ribosomal DNA and four non-coding regions (trnH-psbA, trnL-trnF, atpB-rbcL, rpl16 intron) of chloroplast DNA. We investigated 3, 4, 4 and 11 populations in, Ecuador, Bolivia, Argentina, and Chile, respectively, and detected six ITS genotypes (Types A-F) in G. magellanica. Five genotypes (Types A-E) were observed in the northern Andes population (Ecuador and Bolivia); only one ITS genotype (Type F) was observed in the southern Andes population (Chile and Argentina). Phylogenetic analyses showed that the ITS genotypes of the northern and southern Andes populations form different clades with high bootstrap probability. Furthermore, network analysis, analysis of molecular variance, and spatial analysis of molecular variance showed that there were two major clusters (the northern and southern Andes populations) in this species. Furthermore, in chloroplast DNA analysis, three major clades (northern Andes, Chillan, and southern Andes) were inferred from phylogenetic analyses using four non-coding regions, a finding that was supported by the above three types of analysis. The Chillan clade is the northernmost population in the southern Andes populations. With the exception of the Chillan clade (Chillan population), results of nuclear DNA and chloroplast DNA analyses were consistent. Both markers showed that the northern and southern Andes populations of G. magellanica were genetically different from each other. This type of clear phylogeographical structure was supported by PERMUT analysis according to Pons & Petit (1995, 1996). Moreover, based on our preliminary estimation that is based on the ITS sequences, the northern and southern Andes clades diverged ~0.63-3 million years ago, during a period of upheaval in the Andes. This suggests
A worldwide phylogeography for the human X chromosome.
Directory of Open Access Journals (Sweden)
Simone S Santos-Lopes
Full Text Available BACKGROUND: We reasoned that by identifying genetic markers on human X chromosome regions where recombination is rare or absent, we should be able to construct X chromosome genealogies analogous to those based on Y chromosome and mitochondrial DNA polymorphisms, with the advantage of providing information about both male and female components of the population. METHODOLOGY/PRINCIPAL FINDINGS: We identified a 47 Kb interval containing an Alu insertion polymorphism (DXS225 and four microsatellites in complete linkage disequilibrium in a low recombination rate region of the long arm of the human X chromosome. This haplotype block was studied in 667 males from the HGDP-CEPH Human Genome Diversity Panel. The haplotypic diversity was highest in Africa (0.992+/-0.0025 and lowest in the Americas (0.839+/-0.0378, where no insertion alleles of DXS225 were observed. Africa shared few haplotypes with other geographical areas, while those exhibited significant sharing among themselves. Median joining networks revealed that the African haplotypes were numerous, occupied the periphery of the graph and had low frequency, whereas those from the other continents were few, central and had high frequency. Altogether, our data support a single origin of modern man in Africa and migration to occupy the other continents by serial founder effects. Coalescent analysis permitted estimation of the time of the most recent common ancestor as 182,000 years (56,700-479,000 and the estimated time of the DXS225 Alu insertion of 94,400 years (24,300-310,000. These dates are fully compatible with the current widely accepted scenario of the origin of modern mankind in Africa within the last 195,000 years and migration out-of-Africa circa 55,000-65,000 years ago. CONCLUSIONS/SIGNIFICANCE: A haplotypic block combining an Alu insertion polymorphism and four microsatellite markers on the human X chromosome is a useful marker to evaluate genetic diversity of human populations and
Cross-view gait recognition using joint Bayesian
Li, Chao; Sun, Shouqian; Chen, Xiaoyu; Min, Xin
2017-07-01
Human gait, as a soft biometric, helps to recognize people by walking. To further improve the recognition performance under cross-view condition, we propose Joint Bayesian to model the view variance. We evaluated our prosed method with the largest population (OULP) dataset which makes our result reliable in a statically way. As a result, we confirmed our proposed method significantly outperformed state-of-the-art approaches for both identification and verification tasks. Finally, sensitivity analysis on the number of training subjects was conducted, we find Joint Bayesian could achieve competitive results even with a small subset of training subjects (100 subjects). For further comparison, experimental results, learning models, and test codes are available.
Support agnostic Bayesian matching pursuit for block sparse signals
Masood, Mudassir
2013-05-01
A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.
Sparse reconstruction using distribution agnostic bayesian matching pursuit
Masood, Mudassir
2013-11-01
A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.
Lopes, Camila Madeira Tavares; Menna-Barreto, Rubem Figueiredo Sadok; Pavan, Márcio Galvão; Pereira, Mirian Cláudia De Souza; Roque, André Luiz R
2018-01-01
Didelphis spp. are a South American marsupial species that are among the most ancient hosts for the Trypanosoma spp. We characterise a new species (Trypanosoma janseni n. sp.) isolated from the spleen and liver tissues of Didelphis aurita in the Atlantic Rainforest of Rio de Janeiro, Brazil. The parasites were isolated and a growth curve was performed in NNN and Schneider's media containing 10% foetal bovine serum. Parasite morphology was evaluated via light microscopy on Giemsa-stained culture smears, as well as scanning and transmission electron microscopy. Molecular taxonomy was based on a partial region (737-bp) of the small subunit (18S) ribosomal RNA gene and 708 bp of the nuclear marker, glycosomal glyceraldehyde-3-phosphate dehydrogenase (gGAPDH) genes. Maximum likelihood and Bayesian inference methods were used to perform a species coalescent analysis and to generate individual and concatenated gene trees. Divergence times among species that belong to the T. cruzi clade were also inferred. In vitro growth curves demonstrated a very short log phase, achieving a maximum growth rate at day 3 followed by a sharp decline. Only epimastigote forms were observed under light and scanning microscopy. Transmission electron microscopy analysis showed structures typical to Trypanosoma spp., except one structure that presented as single-membraned, usually grouped in stacks of three or four. Phylogeography analyses confirmed the distinct species status of T. janseni n. sp. within the T. cruzi clade. Trypanosoma janseni n. sp. clusters with T. wauwau in a well-supported clade, which is exclusive and monophyletic. The separation of the South American T. wauwau + T. janseni coincides with the separation of the Southern Super Continent. This clade is a sister group of the trypanosomes found in Australian marsupials and its discovery sheds light on the initial diversification process based on what we currently know about the T. cruzi clade.
Quantifying Uncertainty in Near Surface Electromagnetic Imaging Using Bayesian Methods
Blatter, D. B.; Ray, A.; Key, K.
2017-12-01
Geoscientists commonly use electromagnetic methods to image the Earth's near surface. Field measurements of EM fields are made (often with the aid an artificial EM source) and then used to infer near surface electrical conductivity via a process known as inversion. In geophysics, the standard inversion tool kit is robust and can provide an estimate of the Earth's near surface conductivity that is both geologically reasonable and compatible with the measured field data. However, standard inverse methods struggle to provide a sense of the uncertainty in the estimate they provide. This is because the task of finding an Earth model that explains the data to within measurement error is non-unique - that is, there are many, many such models; but the standard methods provide only one "answer." An alternative method, known as Bayesian inversion, seeks to explore the full range of Earth model parameters that can adequately explain the measured data, rather than attempting to find a single, "ideal" model. Bayesian inverse methods can therefore provide a quantitative assessment of the uncertainty inherent in trying to infer near surface conductivity from noisy, measured field data. This study applies a Bayesian inverse method (called trans-dimensional Markov chain Monte Carlo) to transient airborne EM data previously collected over Taylor Valley - one of the McMurdo Dry Valleys in Antarctica. Our results confirm the reasonableness of previous estimates (made using standard methods) of near surface conductivity beneath Taylor Valley. In addition, we demonstrate quantitatively the uncertainty associated with those estimates. We demonstrate that Bayesian inverse methods can provide quantitative uncertainty to estimates of near surface conductivity.
A Bayesian method for detecting stellar flares
Pitkin, M.; Williams, D.; Fletcher, L.; Grant, S. D. T.
2014-12-01
We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light-curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model required to fit underlying light-curve variations in the data, which could otherwise partially mimic a flare. We characterize the false alarm probability and efficiency of this method under the assumption that any unmodelled noise in the data is Gaussian, and compare it with a simpler thresholding method based on that used in Walkowicz et al. We find our method has a significant increase in detection efficiency for low signal-to-noise ratio (S/N) flares. For a conservative false alarm probability our method can detect 95 per cent of flares with S/N less than 20, as compared to S/N of 25 for the simpler method. We also test how well the assumption of Gaussian noise holds by applying the method to a selection of `quiet' Kepler stars. As an example we have applied our method to a selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars with a total of 1873 flares after vetos have been applied. For these flares we have made preliminary characterizations of their durations and and S/N.
Czech Academy of Sciences Publication Activity Database
Mazoch, Vladimír; Mikula, Ondřej; Bryja, Josef; Konvičková, Hana; Russo, I.-R.; Verheyen, E.; Šumbera, R.
(2018) ISSN 0025-1461 R&D Projects: GA ČR GAP506/10/0983; GA ČR GA15-20229S Institutional support: RVO:68081766 Keywords : Aethomys chrysophilus * Aethomys ineptus * phylogeography * Plio-Pleistocene climate changes * Zambezian bioregion Subject RIV: EG - Zoology Impact factor: 0.805, year: 2016
Czech Academy of Sciences Publication Activity Database
Černý, Viktor; Fernandes, V.; Costa, M. D.; Hájek, Martin; Mulligan, C. J.; Pereira, L.
2009-01-01
Roč. 9, č. 63 (2009), s. 1-9 ISSN 1471-2148 R&D Projects: GA ČR GA206/08/1587 Institutional research plan: CEZ:AV0Z80020508 Keywords : migration * Chadic * phylogeography Subject RIV: AC - Archeology, Anthropology, Ethnology Impact factor: 4.294, year: 2009 http://www.biomedcentral.com/1471-2148/9/63
Czech Academy of Sciences Publication Activity Database
Nicolas, V.; Bryja, Josef; Akpatou, B.; Konečný, Adam; Lecompte, E.; Colyn, M.; Lalis, A.; Couloux, A.; Denys, C.; Granjon, L.
2008-01-01
Roč. 17, č. 23 (2008), s. 5118-5134 ISSN 0962-1083 R&D Projects: GA AV ČR IAA6093404 Institutional research plan: CEZ:AV0Z60930519 Keywords : Africa * cytochrome b * phylogeography Subject RIV: EH - Ecology, Behaviour Impact factor: 5.325, year: 2008
Czech Academy of Sciences Publication Activity Database
Jacquet, F.; Denys, C.; Verheyen, E.; Bryja, Josef; Hutterer, R.; Kerbis Peterhans, J. C.; Stanley, W. T.; Goodman, S. M.; Couloux, A.; Colyn, M.; Nicolas, V.
2015-01-01
Roč. 15, č. 71 (2015), s. 71 ISSN 1471-2148 R&D Projects: GA ČR GAP506/10/0983 Institutional support: RVO:68081766 Keywords : Crocidura olivieri * Diversification * Forest refuge * Molecular dating * Phylogeography * Pleistocene climate changes * Riverine barrier * Soricidae * Systematics Subject RIV: EG - Zoology Impact factor: 3.406, year: 2015
A Bayesian Reflection on Surfaces
Directory of Open Access Journals (Sweden)
David R. Wolf
1999-10-01
Full Text Available Abstract: The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a continuous object and not representable in a finite manner; the tradeoff between accuracy of representation in terms of information learned, and memory or storage capacity in bits; the approximation of probability distributions so that a maximal amount of information about the object being inferred is preserved; an information theoretic justification for multigrid methodology. The maximally informative field inference framework is described in full generality and denoted the Generalized Kalman Filter. The Generalized Kalman Filter allows the update of field knowledge from previous knowledge at any scale, and new data, to new knowledge at any other scale. An application example instance, the inference of continuous surfaces from measurements (for example, camera image data, is presented.
Attention in a bayesian framework
DEFF Research Database (Denmark)
Whiteley, Louise Emma; Sahani, Maneesh
2012-01-01
, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental...... selective and integrative roles, and thus cannot be easily extended to complex environments. We suggest that the resource bottleneck stems from the computational intractability of exact perceptual inference in complex settings, and that attention reflects an evolved mechanism for approximate inference which...... can be shaped to refine the local accuracy of perception. We show that this approach extends the simple picture of attention as prior, so as to provide a unified and computationally driven account of both selective and integrative attentional phenomena....
On Bayesian System Reliability Analysis
Energy Technology Data Exchange (ETDEWEB)
Soerensen Ringi, M
1995-05-01
The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person`s state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs.
Nonparametric Bayesian inference in biostatistics
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...
On Bayesian System Reliability Analysis
International Nuclear Information System (INIS)
Soerensen Ringi, M.
1995-01-01
The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person's state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs
Bayesian estimation in homodyne interferometry
International Nuclear Information System (INIS)
Olivares, Stefano; Paris, Matteo G A
2009-01-01
We address phase-shift estimation by means of squeezed vacuum probe and homodyne detection. We analyse Bayesian estimator, which is known to asymptotically saturate the classical Cramer-Rao bound to the variance, and discuss convergence looking at the a posteriori distribution as the number of measurements increases. We also suggest two feasible adaptive methods, acting on the squeezing parameter and/or the homodyne local oscillator phase, which allow us to optimize homodyne detection and approach the ultimate bound to precision imposed by the quantum Cramer-Rao theorem. The performances of our two-step methods are investigated by means of Monte Carlo simulated experiments with a small number of homodyne data, thus giving a quantitative meaning to the notion of asymptotic optimality.
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.
Bayesian networks in educational assessment
Almond, Russell G; Steinberg, Linda S; Yan, Duanli; Williamson, David M
2015-01-01
Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as ...
Survival Bayesian Estimation of Exponential-Gamma Under Linex Loss Function
Rizki, S. W.; Mara, M. N.; Sulistianingsih, E.
2017-06-01
This paper elaborates a research of the cancer patients after receiving a treatment in cencored data using Bayesian estimation under Linex Loss function for Survival Model which is assumed as an exponential distribution. By giving Gamma distribution as prior and likelihood function produces a gamma distribution as posterior distribution. The posterior distribution is used to find estimatior {\\hat{λ }}BL by using Linex approximation. After getting {\\hat{λ }}BL, the estimators of hazard function {\\hat{h}}BL and survival function {\\hat{S}}BL can be found. Finally, we compare the result of Maximum Likelihood Estimation (MLE) and Linex approximation to find the best method for this observation by finding smaller MSE. The result shows that MSE of hazard and survival under MLE are 2.91728E-07 and 0.000309004 and by using Bayesian Linex worths 2.8727E-07 and 0.000304131, respectively. It concludes that the Bayesian Linex is better than MLE.
Of mice and (Viking?) men: phylogeography of British and Irish house mice.
Searle, Jeremy B; Jones, Catherine S; Gündüz, Islam; Scascitelli, Moira; Jones, Eleanor P; Herman, Jeremy S; Rambau, R Victor; Noble, Leslie R; Berry, R J; Giménez, Mabel D; Jóhannesdóttir, Fríoa
2009-01-22
The west European subspecies of house mouse (Mus musculus domesticus) has gained much of its current widespread distribution through commensalism with humans. This means that the phylogeography of M. m. domesticus should reflect patterns of human movements. We studied restriction fragment length polymorphism (RFLP) and DNA sequence variations in mouse mitochondrial (mt) DNA throughout the British Isles (328 mice from 105 localities, including previously published data). There is a major mtDNA lineage revealed by both RFLP and sequence analyses, which is restricted to the northern and western peripheries of the British Isles, and also occurs in Norway. This distribution of the 'Orkney' lineage fits well with the sphere of influence of the Norwegian Vikings and was probably generated through inadvertent transport by them. To form viable populations, house mice would have required large human settlements such as the Norwegian Vikings founded. The other parts of the British Isles (essentially most of mainland Britain) are characterized by house mice with different mtDNA sequences, some of which are also found in Germany, and which probably reflect both Iron Age movements of people and mice and earlier development of large human settlements. MtDNA studies on house mice have the potential to reveal novel aspects of human history.
A primer on the phylogeography of Lagothrix lagotricha (sensu Fooden) in northern South America.
Botero, Sergio; Stevenson, Pablo R; Di Fiore, Anthony
2015-01-01
The taxonomic history of the genus Lagothrix is complex, with molecular and morphological assessments giving conflicting results for the separation between its taxa. Phylogeographic studies of the most widely distributed species, Lagothrix lagotricha, have only been attempted recently and are limited to few individuals per collection site, many of which were captive making their geographical origin dubious. There is debate regarding the possibility of raising subspecies of Lagothrix lagotricha to the species level, therefore the geographical origin of samples is particularly relevant. In the present work we revisit the intraspecific phylogeography of L. lagotricha from northwestern South America, including the subspecies L. l. poeppiggi, L. l. lagotricha and L. l. lugens (sensu Fooden, 1963), using DNA sequence data from hypervariable region I of the mitochondrial control region (D-loop HVI). Our results suggest a complex picture in which there are well delimited evolutionary units that, nonetheless, do not correlate well with the morphological variation used to support the current delimitation of taxa. Additionally, we corroborate previous results showing a lack of reciprocal monophyly between the putative subspecies of Lagothrix lagotricha, and we propose that this may be due to ancestral polymorphism that has been maintained following the recent spread of woolly monkeys throughout the western Amazonian lowlands and into the inter-Andean region of Colombia. Copyright © 2014 Elsevier Inc. All rights reserved.
Pangaea and the Out-of-Africa Model of Varicella-Zoster Virus Evolution and Phylogeography.
Grose, Charles
2012-09-01
The goal of this minireview is to provide an overview of varicella-zoster virus (VZV) phylogenetics and phylogeography when placed in the broad context of geologic time. Planet Earth was formed over 4 billion years ago, and the supercontinent Pangaea coalesced around 400 million years ago (mya). Based on detailed tree-building models, the base of the phylogenetic tree of the Herpesviridae family has been estimated at 400 mya. Subsequently, Pangaea split into Laurasia and Gondwanaland; in turn, Africa rifted from Gondwanaland. Based on available data, the hypothesis of this minireview is that the ancestral alphaherpesvirus VZV coevolved in simians, apes, and hominins in Africa. When anatomically modern humans first crossed over the Red Sea 60,000 years ago, VZV was carried along in their dorsal root ganglia. Currently, there are five VZV clades, distinguishable by single nucleotide polymorphisms. These clades likely represent continued VZV coevolution, as humans with latent VZV infection left Arabia and dispersed into Asia (clades 2 and 5) and Europe (clades 1, 3, and 4). The prototype VZV sequence contains nearly 125,000 bp, divided into 70 open reading frames. Generally, isolates within a clade display >99.9% identity to one another, while members of one clade compared to a second clade show 99.8% identity to one another. Recently, four different VZV genotypes that do not segregate into the previously defined five clades have been identified, a result indicating a wider than anticipated diversity among newly collected VZV strains around the world.
Directory of Open Access Journals (Sweden)
Jonathan B Puritz
Full Text Available The field of phylogeography has long since realized the need and utility of incorporating nuclear DNA (nDNA sequences into analyses. However, the use of nDNA sequence data, at the population level, has been hindered by technical laboratory difficulty, sequencing costs, and problematic analytical methods dealing with genotypic sequence data, especially in non-model organisms. Here, we present a method utilizing the 454 GS-FLX Titanium pyrosequencing platform with the capacity to simultaneously sequence two species of sea star (Meridiastra calcar and Parvulastra exigua at five different nDNA loci across 16 different populations of 20 individuals each per species. We compare results from 3 populations with traditional Sanger sequencing based methods, and demonstrate that this next-generation sequencing platform is more time and cost effective and more sensitive to rare variants than Sanger based sequencing. A crucial advantage is that the high coverage of clonally amplified sequences simplifies haplotype determination, even in highly polymorphic species. This targeted next-generation approach can greatly increase the use of nDNA sequence loci in phylogeographic and population genetic studies by mitigating many of the time, cost, and analytical issues associated with highly polymorphic, diploid sequence markers.
Phylogeography of mitochondrial DNA variation in brown bears and polar bears
Shields, Gerald F.; Adams, Deborah; Garner, Gerald W.; Labelle, Martine; Pietsch, Jacy; Ramsay, Malcolm; Schwartz, Charles; Titus, Kimberly; Williamson, Scott
2000-01-01
We analyzed 286 nucleotides of the middle portion of the mitochondrial cytochrome b gene of 61 brown bears from three locations in Alaska and 55 polar bears from Arctic Canada and Arctic Siberia to test our earlier observations of paraphyly between polar bears and brown bears as well as to test the extreme uniqueness of mitochondrial DNA types of brown bears on Admiralty, Baranof, and Chichagof (ABC) islands of southeastern Alaska. We also investigated the phylogeography of brown bears of Alaska's Kenai Peninsula in relation to other Alaskan brown bears because the former are being threatened by increased human development. We predicted that: (1) mtDNA paraphyly between brown bears and polar bears would be upheld, (2) the mtDNA uniqueness of brown bears of the ABC islands would be upheld, and (3) brown bears of the Kenai Peninsula would belong to either clade II or clade III of brown bears of our earlier studies of mtDNA. All of our predictions were upheld through the analysis of these additional samples.
Phylogeography of mitochondrial DNA variation in brown bears and polar bears.
Shields, G F; Adams, D; Garner, G; Labelle, M; Pietsch, J; Ramsay, M; Schwartz, C; Titus, K; Williamson, S
2000-05-01
We analyzed 286 nucleotides of the middle portion of the mitochondrial cytochrome b gene of 61 brown bears from three locations in Alaska and 55 polar bears from Arctic Canada and Arctic Siberia to test our earlier observations of paraphyly between polar bears and brown bears as well as to test the extreme uniqueness of mitochondrial DNA types of brown bears on Admiralty, Baranof, and Chichagof (ABC) islands of southeastern Alaska. We also investigated the phylogeography of brown bears of Alaska's Kenai Peninsula in relation to other Alaskan brown bears because the former are being threatened by increased human development. We predicted that: (1) mtDNA paraphyly between brown bears and polar bears would be upheld, (2) the mtDNA uniqueness of brown bears of the ABC islands would be upheld, and (3) brown bears of the Kenai Peninsula would belong to either clade II or clade III of brown bears of our earlier studies of mtDNA. All of our predictions were upheld through the analysis of these additional samples. Copyright 2000 Academic Press.
Paddock, Christopher D; Denison, Amy M; Lash, R Ryan; Liu, Lindy; Bollweg, Brigid C; Dahlgren, F Scott; Kanamura, Cristina T; Angerami, Rodrigo N; Pereira dos Santos, Fabiana C; Brasil Martines, Roosecelis; Karpathy, Sandor E
2014-09-01
Rocky Mountain spotted fever (RMSF), a tick-borne zoonosis caused by Rickettsia rickettsii, is among the deadliest of all infectious diseases. To identify the distribution of various genotypes of R. rickettsii associated with fatal RMSF, we applied molecular typing methods to samples of DNA extracted from formalin-fixed, paraffin-embedded tissue specimens obtained at autopsy from 103 case-patients from seven countries who died of RMSF. Complete sequences of one or more intergenic regions were amplified from tissues of 30 (29%) case-patients and revealed a distribution of genotypes consisting of four distinct clades, including the Hlp clade, regarded previously as a non-pathogenic strain of R. rickettsii. Distinct phylogeographic patterns were identified when composite case-patient and reference strain data were mapped to the state and country of origin. The phylogeography of R. rickettsii is likely determined by ecological and environmental factors that exist independently of the distribution of a particular tick vector. © The American Society of Tropical Medicine and Hygiene.
Directory of Open Access Journals (Sweden)
Vanderly Andrade-Souza
Full Text Available High morphological homogeneity and cryptic speciation may cause the diversity within Simuliidae to be underestimated. Recent molecular studies on population genetics and phylogeography have contributed to reveal which factors influenced the diversity within this group. This study aimed at examining the genetic diversity of Simulium hirtipupa Lutz, 1910 in populations from the biomes Caatinga, Cerrado, and Atlantic Forest. In this study, we carried out phylogeographic and population genetic analyses using a fragment of the mitochondrial gene COI. The 19 populations studied were clustered into seven groups, most of which are associated with geography indicating certain genetic structure. The northern region of the state of Minas Gerais is most likely the center of origin of this species. The average intergroup genetic distance was 3.7%, indicating the presence of cryptic species. The species tree as well as the haplotype network recovered all groups forming two major groups: the first comprises groups Gr-Bahia (in which the São Francisco river has not acted as geographical barrier, Gr-Pernambuco, and Gr-Mato Grosso do Sul. The second included groups comprising populations of the states of Goiás, Tocantins, Minas Gerais, Bahia, São Paulo, and Espírito Santo. The mismatch distribution for groups was consistent with the model of demographic expansion, except for the Gr-Central-East_1 group. The diversification in this group occurred about 1.19 Mya during the Pleistocene, influenced by paleoclimatic oscillations during the Quaternary glacial cycles.
Robust bayesian analysis of an autoregressive model with ...
African Journals Online (AJOL)
In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...
Bayesian models a statistical primer for ecologists
Hobbs, N Thompson
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili
Evolutionary history and phylogeography of rabies viruses associated with outbreaks in Trinidad.
Directory of Open Access Journals (Sweden)
Janine F R Seetahal
Full Text Available Bat rabies is an emerging disease of public health significance in the Americas. The Caribbean island of Trinidad experiences periodic outbreaks within the livestock population. We performed molecular characterisation of Trinidad rabies virus (RABV and used a Bayesian phylogeographic approach to investigate the extent to which outbreaks are a result of in situ evolution versus importation of virus from the nearby South American mainland. Trinidadian RABV sequences were confirmed as bat variant and clustered with Desmodus rotundus (vampire bat related sequences. They fell into two largely temporally defined lineages designated Trinidad I and II. The Trinidad I lineage which included sequences from 1997-2000 (all but two of which were from the northeast of the island was most closely related to RABV from Ecuador (2005, 2007, French Guiana (1990 and Venezuela (1993, 1994. Trinidad II comprised sequences from the southwest of the island, which clustered into two groups: Trinidad IIa, which included one sequence each from 2000 and 2007, and Trinidad IIb including all 2010 sequences. The Trinidad II sequences were most closely related to sequences from Brazil (1999, 2004 and Uruguay (2007, 2008. Phylogeographic analyses support three separate RABV introductions from the mainland from which each of the three Trinidadian lineages arose. The estimated dates for the introductions and subsequent lineage expansions suggest periods of in situ evolution within Trinidad following each introduction. These data also indicate co-circulation of Trinidad lineage I and IIa during 2000. In light of these findings and the likely vampire bat origin of Trinidadian RABV, further studies should be conducted to investigate the relationship between RABV spatiotemporal dynamics and vampire bat population ecology, in particular any movement between the mainland and Trinidad.
Directory of Open Access Journals (Sweden)
Chris A Hamilton
Full Text Available The primary objective of this study is to reconstruct the phylogeny of the hentzi species group and sister species in the North American tarantula genus, Aphonopelma, using a set of mitochondrial DNA markers that include the animal "barcoding gene". An mtDNA genealogy is used to consider questions regarding species boundary delimitation and to evaluate timing of divergence to infer historical biogeographic events that played a role in shaping the present-day diversity and distribution. We aimed to identify potential refugial locations, directionality of range expansion, and test whether A. hentzi post-glacial expansion fit a predicted time frame.A Bayesian phylogenetic approach was used to analyze a 2051 base pair (bp mtDNA data matrix comprising aligned fragments of the gene regions CO1 (1165 bp and ND1-16S (886 bp. Multiple species delimitation techniques (DNA tree-based methods, a "barcode gap" using percent of pairwise sequence divergence (uncorrected p-distances, and the GMYC method consistently recognized a number of divergent and genealogically exclusive groups.The use of numerous species delimitation methods, in concert, provide an effective approach to dissecting species boundaries in this spider group; as well they seem to provide strong evidence for a number of nominal, previously undiscovered, and cryptic species. Our data also indicate that Pleistocene habitat fragmentation and subsequent range expansion events may have shaped contemporary phylogeographic patterns of Aphonopelma diversity in the southwestern United States, particularly for the A. hentzi species group. These findings indicate that future species delimitation approaches need to be analyzed in context of a number of factors, such as the sampling distribution, loci used, biogeographic history, breadth of morphological variation, ecological factors, and behavioral data, to make truly integrative decisions about what constitutes an evolutionary lineage recognized as a
Evolutionary History and Phylogeography of Rabies Viruses Associated with Outbreaks in Trinidad
Seetahal, Janine F. R.; Velasco-Villa, Andres; Allicock, Orchid M.; Adesiyun, Abiodun A.; Bissessar, Joseph; Amour, Kirk; Phillip-Hosein, Annmarie; Marston, Denise A.; McElhinney, Lorraine M.; Shi, Mang; Wharwood, Cheryl-Ann; Fooks, Anthony R.; Carrington, Christine V. F.
2013-01-01
Bat rabies is an emerging disease of public health significance in the Americas. The Caribbean island of Trinidad experiences periodic outbreaks within the livestock population. We performed molecular characterisation of Trinidad rabies virus (RABV) and used a Bayesian phylogeographic approach to investigate the extent to which outbreaks are a result of in situ evolution versus importation of virus from the nearby South American mainland. Trinidadian RABV sequences were confirmed as bat variant and clustered with Desmodus rotundus (vampire bat) related sequences. They fell into two largely temporally defined lineages designated Trinidad I and II. The Trinidad I lineage which included sequences from 1997–2000 (all but two of which were from the northeast of the island) was most closely related to RABV from Ecuador (2005, 2007), French Guiana (1990) and Venezuela (1993, 1994). Trinidad II comprised sequences from the southwest of the island, which clustered into two groups: Trinidad IIa, which included one sequence each from 2000 and 2007, and Trinidad IIb including all 2010 sequences. The Trinidad II sequences were most closely related to sequences from Brazil (1999, 2004) and Uruguay (2007, 2008). Phylogeographic analyses support three separate RABV introductions from the mainland from which each of the three Trinidadian lineages arose. The estimated dates for the introductions and subsequent lineage expansions suggest periods of in situ evolution within Trinidad following each introduction. These data also indicate co-circulation of Trinidad lineage I and IIa during 2000. In light of these findings and the likely vampire bat origin of Trinidadian RABV, further studies should be conducted to investigate the relationship between RABV spatiotemporal dynamics and vampire bat population ecology, in particular any movement between the mainland and Trinidad. PMID:23991230
Bayesian geostatistical modeling of leishmaniasis incidence in Brazil.
Directory of Open Access Journals (Sweden)
Dimitrios-Alexios Karagiannis-Voules
Full Text Available BACKGROUND: Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries. METHODOLOGY: We applied Bayesian geostatistical negative binomial models to analyze reported incidence data of cutaneous and visceral leishmaniasis in Brazil covering a 10-year period (2001-2010. Particular emphasis was placed on spatial and temporal patterns. The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference. Bayesian variable selection was employed to determine the most important climatic, environmental, and socioeconomic predictors of cutaneous and visceral leishmaniasis. PRINCIPAL FINDINGS: For both types of leishmaniasis, precipitation and socioeconomic proxies were identified as important risk factors. The predicted number of cases in 2010 were 30,189 (standard deviation [SD]: 7,676 for cutaneous leishmaniasis and 4,889 (SD: 288 for visceral leishmaniasis. Our risk maps predicted the highest numbers of infected people in the states of Minas Gerais and Pará for visceral and cutaneous leishmaniasis, respectively. CONCLUSIONS/SIGNIFICANCE: Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence.
Bayesian LASSO, scale space and decision making in association genetics.
Pasanen, Leena; Holmström, Lasse; Sillanpää, Mikko J
2015-01-01
LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.
Directory of Open Access Journals (Sweden)
Maykon Passos Cristiano
Full Text Available Past climate changes often have influenced the present distribution and intraspecific genetic diversity of organisms. The objective of this study was to investigate the phylogeography and historical demography of populations of Acromyrmex striatus (Roger, 1863, a leaf-cutting ant species restricted to the open plains of South America. Additionally, we modeled the distribution of this species to predict its contemporary and historic habitat. From the partial sequences of the mitochondrial gene cytochrome oxidase I of 128 A. striatus workers from 38 locations we estimated genetic diversity and inferred historical demography, divergence time, and population structure. The potential distribution areas of A. striatus for current and quaternary weather conditions were modeled using the maximum entropy algorithm. We identified a total of 58 haplotypes, divided into five main haplogroups. The analysis of molecular variance (AMOVA revealed that the largest proportion of genetic variation is found among the groups of populations. Paleodistribution models suggest that the potential habitat of A. striatus may have decreased during the Last Interglacial Period (LIG and expanded during the Last Maximum Glacial (LGM. Overall, the past potential distribution recovered by the model comprises the current potential distribution of the species. The general structuring pattern observed was consistent with isolation by distance, suggesting a balance between gene flow and drift. Analysis of historical demography showed that populations of A. striatus had remained constant throughout its evolutionary history. Although fluctuations in the area of their potential historic habitat occurred during quaternary climate changes, populations of A. striatus are strongly structured geographically. However, explicit barriers to gene flow have not been identified. These findings closely match those in Mycetophylax simplex, another ant species that in some areas occurs in sympatry
Bilgin, Raşit; Gürün, Kanat; Rebelo, Hugo; Puechmaille, Sebastien J; Maracı, Öncü; Presetnik, Primoz; Benda, Petr; Hulva, Pavel; Ibáñez, Carlos; Hamidovic, Daniela; Fressel, Norma; Horáček, Ivan; Karataş, Ayşegül; Karataş, Ahmet; Allegrini, Benjamin; Georgiakakis, Panagiotis; Gazaryan, Suren; Nagy, Zoltan L; Abi-Said, Mounir; Lučan, Radek K; Bartonička, Tomáš; Nicolaou, Haris; Scaravelli, Dino; Karapandža, Branko; Uhrin, Marcel; Paunović, Milan; Juste, Javier
2016-06-01
The isolation of populations in the Iberian, Italian and Balkan peninsulas during the ice ages define four main paradigms that explain much of the known distribution of intraspecific genetic diversity in Europe. In this study we investigated the phylogeography of a wide-spread bat species, the bent-winged bat, Miniopterus schreibersii around the Mediterranean basin and in the Caucasus. Environmental Niche Modeling (ENM) analysis was applied to predict both the current distribution of the species and its distribution during the last glacial maximum (LGM). The combination of genetics and ENM results suggest that the populations of M. schreibersii in Europe, the Caucasus and Anatolia went extinct during the LGM, and the refugium for the species was a relatively small area to the east of the Levantine Sea, corresponding to the Mediterranean coasts of present-day Syria, Lebanon, Israel, and northeastern and northwestern Egypt. Subsequently the species first repopulated Anatolia, diversified there, and afterwards expanded into the Caucasus, continental Europe and North Africa after the end of the LGM. The fossil record in Iberia and the ENM results indicate continuous presence of Miniopterus in this peninsula that most probably was related to the Maghrebian lineage during the LGM, which did not persist afterwards. Using our results combined with similar findings in previous studies, we propose a new paradigm explaining the general distribution of genetic diversity in Europe involving the recolonization of the continent, with the main contribution from refugial populations in Anatolia and the Middle East. The study shows how genetics and ENM approaches can complement each other in providing a more detailed picture of intraspecific evolution. Copyright © 2016 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Agostino Leone
2017-12-01
Full Text Available Background The blue shark (Prionace glauca, Linnaeus 1758 is one of the most abundant epipelagic shark inhabiting all the oceans except the poles, including the Mediterranean Sea, but its genetic structure has not been confirmed at basin and interoceanic distances. Past tagging programs in the Atlantic Ocean failed to find evidence of migration of blue sharks between the Mediterranean and the adjacent Atlantic, despite the extreme vagility of the species. Although the high rate of by-catch in the Mediterranean basin, to date no genetic study on Mediterranean blue shark was carried out, which constitutes a significant knowledge gap, considering that this population is classified as “Critically Endangered”, unlike its open-ocean counterpart. Methods Blue shark phylogeography and demography in the Mediterranean Sea and North-Eastern Atlantic Ocean were inferred using two mitochondrial genes (Cytb and control region amplified from 207 and 170 individuals respectively, collected from six localities across the Mediterranean and two from the North-Eastern Atlantic. Results Although no obvious pattern of geographical differentiation was apparent from the haplotype network, Φst analyses indicated significant genetic structure among four geographical groups. Demographic analyses suggest that these populations have experienced a constant population expansion in the last 0.4–0.1 million of years. Discussion The weak, but significant, differences in Mediterranean and adjacent North-eastern Atlantic blue sharks revealed a complex phylogeographic structure, which appears to reject the assumption of panmixia across the study area, but also supports a certain degree of population connectivity across the Strait of Gibraltar, despite the lack of evidence of migratory movements observed by tagging data. Analyses of spatial genetic structure in relation to sex-ratio and size could indicate some level of sex/stage biased migratory behaviour.
Bayesian adaptive methods for clinical trials
National Research Council Canada - National Science Library
Berry, Scott M
2011-01-01
.... One is that Bayesian approaches implemented with the majority of their informative content coming from the current data, and not any external prior informa- tion, typically have good frequentist properties (e.g...
A Bayesian approach to model uncertainty
International Nuclear Information System (INIS)
Buslik, A.
1994-01-01
A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given
Structure-based bayesian sparse reconstruction
Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.
2012-01-01
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical
Learning Bayesian networks for discrete data
Liang, Faming
2009-02-01
Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.
Correct Bayesian and frequentist intervals are similar
International Nuclear Information System (INIS)
Atwood, C.L.
1986-01-01
This paper argues that Bayesians and frequentists will normally reach numerically similar conclusions, when dealing with vague data or sparse data. It is shown that both statistical methodologies can deal reasonably with vague data. With sparse data, in many important practical cases Bayesian interval estimates and frequentist confidence intervals are approximately equal, although with discrete data the frequentist intervals are somewhat longer. This is not to say that the two methodologies are equally easy to use: The construction of a frequentist confidence interval may require new theoretical development. Bayesians methods typically require numerical integration, perhaps over many variables. Also, Bayesian can easily fall into the trap of over-optimism about their amount of prior knowledge. But in cases where both intervals are found correctly, the two intervals are usually not very different. (orig.)
Implementing the Bayesian paradigm in risk analysis
International Nuclear Information System (INIS)
Aven, T.; Kvaloey, J.T.
2002-01-01
The Bayesian paradigm comprises a unified and consistent framework for analyzing and expressing risk. Yet, we see rather few examples of applications where the full Bayesian setting has been adopted with specifications of priors of unknown parameters. In this paper, we discuss some of the practical challenges of implementing Bayesian thinking and methods in risk analysis, emphasizing the introduction of probability models and parameters and associated uncertainty assessments. We conclude that there is a need for a pragmatic view in order to 'successfully' apply the Bayesian approach, such that we can do the assignments of some of the probabilities without adopting the somewhat sophisticated procedure of specifying prior distributions of parameters. A simple risk analysis example is presented to illustrate ideas
An overview on Approximate Bayesian computation*
Directory of Open Access Journals (Sweden)
Baragatti Meïli
2014-01-01
Full Text Available Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the most satisfactory approach to intractable likelihood problems. This overview presents recent results since its introduction about ten years ago in population genetics.
Analysis of COSIMA spectra: Bayesian approach
Directory of Open Access Journals (Sweden)
H. J. Lehto
2015-06-01
secondary ion mass spectrometer (TOF-SIMS spectra. The method is applied to the COmetary Secondary Ion Mass Analyzer (COSIMA TOF-SIMS mass spectra where the analysis can be broken into subgroups of lines close to integer mass values. The effects of the instrumental dead time are discussed in a new way. The method finds the joint probability density functions of measured line parameters (number of lines, and their widths, peak amplitudes, integrated amplitudes and positions. In the case of two or more lines, these distributions can take complex forms. The derived line parameters can be used to further calibrate the mass scaling of TOF-SIMS and to feed the results into other analysis methods such as multivariate analyses of spectra. We intend to use the method, first as a comprehensive tool to perform quantitative analysis of spectra, and second as a fast tool for studying interesting targets for obtaining additional TOF-SIMS measurements of the sample, a property unique to COSIMA. Finally, we point out that the Bayesian method can be thought of as a means to solve inverse problems but with forward calculations, only with no iterative corrections or other manipulation of the observed data.
BAYESIAN INFERENCE OF CMB GRAVITATIONAL LENSING
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Anderes, Ethan [Department of Statistics, University of California, Davis, CA 95616 (United States); Wandelt, Benjamin D.; Lavaux, Guilhem [Sorbonne Universités, UPMC Univ Paris 06 and CNRS, UMR7095, Institut d’Astrophysique de Paris, F-75014, Paris (France)
2015-08-01
The Planck satellite, along with several ground-based telescopes, has mapped the cosmic microwave background (CMB) at sufficient resolution and signal-to-noise so as to allow a detection of the subtle distortions due to the gravitational influence of the intervening matter distribution. A natural modeling approach is to write a Bayesian hierarchical model for the lensed CMB in terms of the unlensed CMB and the lensing potential. So far there has been no feasible algorithm for inferring the posterior distribution of the lensing potential from the lensed CMB map. We propose a solution that allows efficient Markov Chain Monte Carlo sampling from the joint posterior of the lensing potential and the unlensed CMB map using the Hamiltonian Monte Carlo technique. The main conceptual step in the solution is a re-parameterization of CMB lensing in terms of the lensed CMB and the “inverse lensing” potential. We demonstrate a fast implementation on simulated data, including noise and a sky cut, that uses a further acceleration based on a very mild approximation of the inverse lensing potential. We find that the resulting Markov Chain has short correlation lengths and excellent convergence properties, making it promising for applications to high-resolution CMB data sets in the future.
Bayesian data assimilation in shape registration
Cotter, C J
2013-03-28
In this paper we apply a Bayesian framework to the problem of geodesic curve matching. Given a template curve, the geodesic equations provide a mapping from initial conditions for the conjugate momentum onto topologically equivalent shapes. Here, we aim to recover the well-defined posterior distribution on the initial momentum which gives rise to observed points on the target curve; this is achieved by explicitly including a reparameterization in the formulation. Appropriate priors are chosen for the functions which together determine this field and the positions of the observation points, the initial momentum p0 and the reparameterization vector field ν, informed by regularity results about the forward model. Having done this, we illustrate how maximum likelihood estimators can be used to find regions of high posterior density, but also how we can apply recently developed Markov chain Monte Carlo methods on function spaces to characterize the whole of the posterior density. These illustrative examples also include scenarios where the posterior distribution is multimodal and irregular, leading us to the conclusion that knowledge of a state of global maximal posterior density does not always give us the whole picture, and full posterior sampling can give better quantification of likely states and the overall uncertainty inherent in the problem. © 2013 IOP Publishing Ltd.
Bayesian probability theory and inverse problems
International Nuclear Information System (INIS)
Kopec, S.
1994-01-01
Bayesian probability theory is applied to approximate solving of the inverse problems. In order to solve the moment problem with the noisy data, the entropic prior is used. The expressions for the solution and its error bounds are presented. When the noise level tends to zero, the Bayesian solution tends to the classic maximum entropy solution in the L 2 norm. The way of using spline prior is also shown. (author)
A Bayesian classifier for symbol recognition
Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick
2007-01-01
URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...
Bayesian Modeling of a Human MMORPG Player
Synnaeve, Gabriel; Bessière, Pierre
2011-03-01
This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes are present. We explain the model in Bayesian programming and show how we could learn the conditional probabilities from data gathered during human-played sessions.
Variations on Bayesian Prediction and Inference
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
Bayesian target tracking based on particle filter
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter (EKF) proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo (MCMC) method, the resampling step, etc novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.
MCMC for parameters estimation by bayesian approach
International Nuclear Information System (INIS)
Ait Saadi, H.; Ykhlef, F.; Guessoum, A.
2011-01-01
This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.
Bayesian Networks for Modeling Dredging Decisions
2011-10-01
years, that algorithms have been developed to solve these problems efficiently. Most modern Bayesian network software uses junction tree (a.k.a. join... software was used to develop the network . This is by no means an exhaustive list of Bayesian network applications, but it is representative of recent...characteristic node (SCN), state- defining node ( SDN ), effect node (EFN), or value node. The five types of nodes can be described as follows: ERDC/EL TR-11
Fully probabilistic design of hierarchical Bayesian models
Czech Academy of Sciences Publication Activity Database
Quinn, A.; Kárný, Miroslav; Guy, Tatiana Valentine
2016-01-01
Roč. 369, č. 1 (2016), s. 532-547 ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Fully probabilistic design * Ideal distribution * Minimum cross-entropy principle * Bayesian conditioning * Kullback-Leibler divergence * Bayesian nonparametric modelling Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.832, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/karny-0463052.pdf
A Bayesian Method for Weighted Sampling
Lo, Albert Y.
1993-01-01
Bayesian statistical inference for sampling from weighted distribution models is studied. Small-sample Bayesian bootstrap clone (BBC) approximations to the posterior distribution are discussed. A second-order property for the BBC in unweighted i.i.d. sampling is given. A consequence is that BBC approximations to a posterior distribution of the mean and to the sampling distribution of the sample average, can be made asymptotically accurate by a proper choice of the random variables that genera...
Directory of Open Access Journals (Sweden)
Shu Wing Ho
2011-12-01
Full Text Available The valuation of options and many other derivative instruments requires an estimation of exante or forward looking volatility. This paper adopts a Bayesian approach to estimate stock price volatility. We find evidence that overall Bayesian volatility estimates more closely approximate the implied volatility of stocks derived from traded call and put options prices compared to historical volatility estimates sourced from IVolatility.com (“IVolatility”. Our evidence suggests use of the Bayesian approach to estimate volatility can provide a more accurate measure of ex-ante stock price volatility and will be useful in the pricing of derivative securities where the implied stock price volatility cannot be observed.
Philosophy and the practice of Bayesian statistics.
Gelman, Andrew; Shalizi, Cosma Rohilla
2013-02-01
A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. © 2012 The British Psychological Society.
Morgan, E R; Clare, E L; Jefferies, R; Stevens, J R
2012-12-01
SUMMARY Molecular phylogeography has revolutionised our ability to infer past biogeographic events from cross-sectional data on current parasite populations. In ecological parasitology, this approach has been used to address fundamental questions concerning host-parasite co-evolution and geographic patterns of spread, and has raised many technical issues and problems of interpretation. For applied parasitologists, the added complexity inherent in adding population genetic structure to perceived parasite distributions can sometimes seem to cloud rather than clarify approaches to control. In this paper, we use case studies firstly to illustrate the potential extent of cryptic diversity in parasite and parasitoid populations, secondly to consider how anthropogenic influences including movement of domestic animals affect the geographic distribution and host associations of parasite genotypes, and thirdly to explore the applied relevance of these processes to parasites of socio-economic importance. The contribution of phylogeographic approaches to deeper understanding of parasite biology in these cases is assessed. Thus, molecular data on the emerging parasites Angiostrongylus vasorum in dogs and wild canids, and the myiasis-causing flies Lucilia spp. in sheep and Cochliomyia hominovorax in humans, lead to clear implications for control efforts to limit global spread. Broader applications of molecular phylogeography to understanding parasite distributions in an era of rapid global change are also discussed.
EXONEST: The Bayesian Exoplanetary Explorer
Directory of Open Access Journals (Sweden)
Kevin H. Knuth
2017-10-01
Full Text Available The fields of astronomy and astrophysics are currently engaged in an unprecedented era of discovery as recent missions have revealed thousands of exoplanets orbiting other stars. While the Kepler Space Telescope mission has enabled most of these exoplanets to be detected by identifying transiting events, exoplanets often exhibit additional photometric effects that can be used to improve the characterization of exoplanets. The EXONEST Exoplanetary Explorer is a Bayesian exoplanet inference engine based on nested sampling and originally designed to analyze archived Kepler Space Telescope and CoRoT (Convection Rotation et Transits planétaires exoplanet mission data. We discuss the EXONEST software package and describe how it accommodates plug-and-play models of exoplanet-associated photometric effects for the purpose of exoplanet detection, characterization and scientific hypothesis testing. The current suite of models allows for both circular and eccentric orbits in conjunction with photometric effects, such as the primary transit and secondary eclipse, reflected light, thermal emissions, ellipsoidal variations, Doppler beaming and superrotation. We discuss our new efforts to expand the capabilities of the software to include more subtle photometric effects involving reflected and refracted light. We discuss the EXONEST inference engine design and introduce our plans to port the current MATLAB-based EXONEST software package over to the next generation Exoplanetary Explorer, which will be a Python-based open source project with the capability to employ third-party plug-and-play models of exoplanet-related photometric effects.
Maximum entropy and Bayesian methods
International Nuclear Information System (INIS)
Smith, C.R.; Erickson, G.J.; Neudorfer, P.O.
1992-01-01
Bayesian probability theory and Maximum Entropy methods are at the core of a new view of scientific inference. These 'new' ideas, along with the revolution in computational methods afforded by modern computers allow astronomers, electrical engineers, image processors of any type, NMR chemists and physicists, and anyone at all who has to deal with incomplete and noisy data, to take advantage of methods that, in the past, have been applied only in some areas of theoretical physics. The title workshops have been the focus of a group of researchers from many different fields, and this diversity is evident in this book. There are tutorial and theoretical papers, and applications in a very wide variety of fields. Almost any instance of dealing with incomplete and noisy data can be usefully treated by these methods, and many areas of theoretical research are being enhanced by the thoughtful application of Bayes' theorem. Contributions contained in this volume present a state-of-the-art overview that will be influential and useful for many years to come
Inverse problems in the Bayesian framework
International Nuclear Information System (INIS)
Calvetti, Daniela; Somersalo, Erkki; Kaipio, Jari P
2014-01-01
The history of Bayesian methods dates back to the original works of Reverend Thomas Bayes and Pierre-Simon Laplace: the former laid down some of the basic principles on inverse probability in his classic article ‘An essay towards solving a problem in the doctrine of chances’ that was read posthumously in the Royal Society in 1763. Laplace, on the other hand, in his ‘Memoirs on inverse probability’ of 1774 developed the idea of updating beliefs and wrote down the celebrated Bayes’ formula in the form we know today. Although not identified yet as a framework for investigating inverse problems, Laplace used the formalism very much in the spirit it is used today in the context of inverse problems, e.g., in his study of the distribution of comets. With the evolution of computational tools, Bayesian methods have become increasingly popular in all fields of human knowledge in which conclusions need to be drawn based on incomplete and noisy data. Needless to say, inverse problems, almost by definition, fall into this category. Systematic work for developing a Bayesian inverse problem framework can arguably be traced back to the 1980s, (the original first edition being published by Elsevier in 1987), although articles on Bayesian methodology applied to inverse problems, in particular in geophysics, had appeared much earlier. Today, as testified by the articles in this special issue, the Bayesian methodology as a framework for considering inverse problems has gained a lot of popularity, and it has integrated very successfully with many traditional inverse problems ideas and techniques, providing novel ways to interpret and implement traditional procedures in numerical analysis, computational statistics, signal analysis and data assimilation. The range of applications where the Bayesian framework has been fundamental goes from geophysics, engineering and imaging to astronomy, life sciences and economy, and continues to grow. There is no question that Bayesian
Cophylogeny reconstruction via an approximate Bayesian computation.
Baudet, C; Donati, B; Sinaimeri, B; Crescenzi, P; Gautier, C; Matias, C; Sagot, M-F
2015-05-01
Despite an increasingly vast literature on cophylogenetic reconstructions for studying host-parasite associations, understanding the common evolutionary history of such systems remains a problem that is far from being solved. Most algorithms for host-parasite reconciliation use an event-based model, where the events include in general (a subset of) cospeciation, duplication, loss, and host switch. All known parsimonious event-based methods then assign a cost to each type of event in order to find a reconstruction of minimum cost. The main problem with this approach is that the cost of the events strongly influences the reconciliation obtained. Some earlier approaches attempt to avoid this problem by finding a Pareto set of solutions and hence by considering event costs under some minimization constraints. To deal with this problem, we developed an algorithm, called Coala, for estimating the frequency of the events based on an approximate Bayesian computation approach. The benefits of this method are 2-fold: (i) it provides more confidence in the set of costs to be used in a reconciliation, and (ii) it allows estimation of the frequency of the events in cases where the data set consists of trees with a large number of taxa. We evaluate our method on simulated and on biological data sets. We show that in both cases, for the same pair of host and parasite trees, different sets of frequencies for the events lead to equally probable solutions. Moreover, often these solutions differ greatly in terms of the number of inferred events. It appears crucial to take this into account before attempting any further biological interpretation of such reconciliations. More generally, we also show that the set of frequencies can vary widely depending on the input host and parasite trees. Indiscriminately applying a standard vector of costs may thus not be a good strategy. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.
A Bayesian analysis of pentaquark signals from CLAS data
Energy Technology Data Exchange (ETDEWEB)
David Ireland; Bryan McKinnon; Dan Protopopescu; Pawel Ambrozewicz; Marco Anghinolfi; G. Asryan; Harutyun Avakian; H. Bagdasaryan; Nathan Baillie; Jacques Ball; Nathan Baltzell; V. Batourine; Marco Battaglieri; Ivan Bedlinski; Ivan Bedlinskiy; Matthew Bellis; Nawal Benmouna; Barry Berman; Angela Biselli; Lukasz Blaszczyk; Sylvain Bouchigny; Sergey Boyarinov; Robert Bradford; Derek Branford; William Briscoe; William Brooks; Volker Burkert; Cornel Butuceanu; John Calarco; Sharon Careccia; Daniel Carman; Liam Casey; Shifeng Chen; Lu Cheng; Philip Cole; Patrick Collins; Philip Coltharp; Donald Crabb; Volker Crede; Natalya Dashyan; Rita De Masi; Raffaella De Vita; Enzo De Sanctis; Pavel Degtiarenko; Alexandre Deur; Richard Dickson; Chaden Djalali; Gail Dodge; Joseph Donnelly; David Doughty; Michael Dugger; Oleksandr Dzyubak; Hovanes Egiyan; Kim Egiyan; Lamiaa Elfassi; Latifa Elouadrhiri; Paul Eugenio; Gleb Fedotov; Gerald Feldman; Ahmed Fradi; Herbert Funsten; Michel Garcon; Gagik Gavalian; Nerses Gevorgyan; Gerard Gilfoyle; Kevin Giovanetti; Francois-Xavier Girod; John Goetz; Wesley Gohn; Atilla Gonenc; Ralf Gothe; Keith Griffioen; Michel Guidal; Nevzat Guler; Lei Guo; Vardan Gyurjyan; Kawtar Hafidi; Hayk Hakobyan; Charles Hanretty; Neil Hassall; F. Hersman; Ishaq Hleiqawi; Maurik Holtrop; Charles Hyde; Yordanka Ilieva; Boris Ishkhanov; Eugeny Isupov; D. Jenkins; Hyon-Suk Jo; John Johnstone; Kyungseon Joo; Henry Juengst; Narbe Kalantarians; James Kellie; Mahbubul Khandaker; Wooyoung Kim; Andreas Klein; Franz Klein; Mikhail Kossov; Zebulun Krahn; Laird Kramer; Valery Kubarovsky; Joachim Kuhn; Sergey Kuleshov; Viacheslav Kuznetsov; Jeff Lachniet; Jean Laget; Jorn Langheinrich; D. Lawrence; Kenneth Livingston; Haiyun Lu; Marion MacCormick; Nikolai Markov; Paul Mattione; Bernhard Mecking; Mac Mestayer; Curtis Meyer; Tsutomu Mibe; Konstantin Mikhaylov; Marco Mirazita; Rory Miskimen; Viktor Mokeev; Brahim Moreno; Kei Moriya; Steven Morrow; Maryam Moteabbed; Edwin Munevar Espitia; Gordon Mutchler; Pawel Nadel-Turonski; Rakhsha Nasseripour; Silvia Niccolai; Gabriel Niculescu; Maria-Ioana Niculescu; Bogdan Niczyporuk; Megh Niroula; Rustam Niyazov; Mina Nozar; Mikhail Osipenko; Alexander Ostrovidov; Kijun Park; Evgueni Pasyuk; Craig Paterson; Sergio Pereira; Joshua Pierce; Nikolay Pivnyuk; Oleg Pogorelko; Sergey Pozdnyakov; John Price; Sebastien Procureur; Yelena Prok; Brian Raue; Giovanni Ricco; Marco Ripani; Barry Ritchie; Federico Ronchetti; Guenther Rosner; Patrizia Rossi; Franck Sabatie; Julian Salamanca; Carlos Salgado; Joseph Santoro; Vladimir Sapunenko; Reinhard Schumacher; Vladimir Serov; Youri Sharabian; Dmitri Sharov; Nikolay Shvedunov; Elton Smith; Lee Smith; Daniel Sober; Daria Sokhan; Aleksey Stavinskiy; Samuel Stepanyan; Stepan Stepanyan; Burnham Stokes; Paul Stoler; Steffen Strauch; Mauro Taiuti; David Tedeschi; Ulrike Thoma; Avtandil Tkabladze; Svyatoslav Tkachenko; Clarisse Tur; Maurizio Ungaro; Michael Vineyard; Alexander Vlassov; Daniel Watts; Lawrence Weinstein; Dennis Weygand; M. Williams; Elliott Wolin; M.H. Wood; Amrit Yegneswaran; Lorenzo Zana; Jixie Zhang; Bo Zhao; Zhiwen Zhao
2008-02-01
We examine the results of two measurements by the CLAS collaboration, one of which claimed evidence for a $\\Theta^{+}$ pentaquark, whilst the other found no such evidence. The unique feature of these two experiments was that they were performed with the same experimental setup. Using a Bayesian analysis we find that the results of the two experiments are in fact compatible with each other, but that the first measurement did not contain sufficient information to determine unambiguously the existence of a $\\Theta^{+}$. Further, we suggest a means by which the existence of a new candidate particle can be tested in a rigorous manner.
Spike-Based Bayesian-Hebbian Learning of Temporal Sequences
DEFF Research Database (Denmark)
Tully, Philip J; Lindén, Henrik; Hennig, Matthias H
2016-01-01
Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed...... in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods...
Count Data On Cancer Death In Ohio A Bayesian Analysis
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Walaa Hamdi
2015-08-01
Full Text Available This paper considers statistical modeling of count data on cancer death in Ohio State. We obtained count data on male and female from a website of the Centers for Disease Control and Prevention and used Bayesian analyses to find suitable models which help us to do inferences and predictions for next year. To assist us in selecting appropriate models we use criteria such as the DIC. In this paper we analyze the data to spatial longitudinal so we can capture possible correlations. Using our analyses we make predictions of the numbers of people who will die with cancer in a future year in Ohio State.
A Bayesian analysis of pentaquark signals from CLAS data
International Nuclear Information System (INIS)
David Ireland; Bryan McKinnon; Dan Protopopescu; Pawel Ambrozewicz; Marco Anghinolfi; G. Asryan; Harutyun Avakian; H. Bagdasaryan; Nathan Baillie; Jacques Ball; Nathan Baltzell; V. Batourine; Marco Battaglieri; Ivan Bedlinski; Ivan Bedlinskiy; Matthew Bellis; Nawal Benmouna; Barry Berman; Angela Biselli; Lukasz Blaszczyk; Sylvain Bouchigny; Sergey Boyarinov; Robert Bradford; Derek Branford; William Briscoe; William Brooks; Volker Burkert; Cornel Butuceanu; John Calarco; Sharon Careccia; Daniel Carman; Liam Casey; Shifeng Chen; Lu Cheng; Philip Cole; Patrick Collins; Philip Coltharp; Donald Crabb; Volker Crede; Natalya Dashyan; Rita De Masi; Raffaella De Vita; Enzo De Sanctis; Pavel Degtiarenko; Alexandre Deur; Richard Dickson; Chaden Djalali; Gail Dodge; Joseph Donnelly; David Doughty; Michael Dugger; Oleksandr Dzyubak; Hovanes Egiyan; Kim Egiyan; Lamiaa Elfassi; Latifa Elouadrhiri; Paul Eugenio; Gleb Fedotov; Gerald Feldman; Ahmed Fradi; Herbert Funsten; Michel Garcon; Gagik Gavalian; Nerses Gevorgyan; Gerard Gilfoyle; Kevin Giovanetti; Francois-Xavier Girod; John Goetz; Wesley Gohn; Atilla Gonenc; Ralf Gothe; Keith Griffioen; Michel Guidal; Nevzat Guler; Lei Guo; Vardan Gyurjyan; Kawtar Hafidi; Hayk Hakobyan; Charles Hanretty; Neil Hassall; F. Hersman; Ishaq Hleiqawi; Maurik Holtrop; Charles Hyde; Yordanka Ilieva; Boris Ishkhanov; Eugeny Isupov; D. Jenkins; Hyon-Suk Jo; John Johnstone; Kyungseon Joo; Henry Juengst; Narbe Kalantarians; James Kellie; Mahbubul Khandaker; et al
2007-01-01
We examine the results of two measurements by the CLAS collaboration, one of which claimed evidence for a Θ + pentaquark, whilst the other found no such evidence. The unique feature of these two experiments was that they were performed with the same experimental setup. Using a Bayesian analysis we find that the results of the two experiments are in fact compatible with each other, but that the first measurement did not contain sufficient information to determine unambiguously the existence of a Θ + . Further, we suggest a means by which the existence of a new candidate particle can be tested in a rigorous manner
Hellgren, Olof; Atkinson, Carter T.; Bensch, Staffan; Albayrak, Tamer; Dimitrov, Dimitar; Ewen, John G.; Kim, Kyeong Soon; Lima, Marcos R.; Martin, Lynn; Palinauskas, Vaidas; Ricklefs, Robert; Sehgal, Ravinder N. M.; Gediminas, Valkiunas; Tsuda, Yoshio; Marzal, Alfonso
2015-01-01
Knowing the genetic variation that occurs in pathogen populations and how it is distributed across geographical areas is essential to understand parasite epidemiology, local patterns of virulence, and evolution of host-resistance. In addition, it is important to identify populations of pathogens that are evolutionarily independent and thus ‘free’ to adapt to hosts and environments. Here, we investigated genetic variation in the globally distributed, highly invasive avian malaria parasite Plasmodium relictum, which has several distinctive mitochondrial haplotyps (cyt b lineages, SGS1, GRW11 and GRW4). The phylogeography of P. relictum was accessed using the highly variable nuclear gene merozoite surface protein 1 (MSP1), a gene linked to the invasion biology of the parasite. We show that the lineage GRW4 is evolutionarily independent of GRW11 and SGS1 whereas GRW11 and SGS1 share MSP1 alleles and thus suggesting the presence of two distinct species (GRW4 versus SGS1 and GRW11). Further, there were significant differences in the global distribution of MSP1 alleles with differences between GRW4 alleles in the New and the Old World. For SGS1, a lineage formerly believed to have both tropical and temperate transmission, there were clear differences in MSP1 alleles transmitted in tropical Africa compared to the temperate regions of Europe and Asia. Further, we highlight the occurrence of multiple MSP1 alleles in GRW4 isolates from the Hawaiian Islands, where the parasite has contributed to declines and extinctions of endemic forest birds since it was introduced. This study stresses the importance of multiple independent loci for understanding patterns of transmission and evolutionary independence across avian malaria parasites.
A holistic picture of Austronesian migrations revealed by phylogeography of Pacific paper mulberry
Chang, Chi-Shan; Liu, Hsiao-Lei; Moncada, Ximena; Seelenfreund, Andrea; Seelenfreund, Daniela; Chung, Kuo-Fang
2015-01-01
The peopling of Remote Oceanic islands by Austronesian speakers is a fascinating and yet contentious part of human prehistory. Linguistic, archaeological, and genetic studies have shown the complex nature of the process in which different components that helped to shape Lapita culture in Near Oceania each have their own unique history. Important evidence points to Taiwan as an Austronesian ancestral homeland with a more distant origin in South China, whereas alternative models favor South China to North Vietnam or a Southeast Asian origin. We test these propositions by studying phylogeography of paper mulberry, a common East Asian tree species introduced and clonally propagated since prehistoric times across the Pacific for making barkcloth, a practical and symbolic component of Austronesian cultures. Using the hypervariable chloroplast ndhF-rpl32 sequences of 604 samples collected from East Asia, Southeast Asia, and Oceanic islands (including 19 historical herbarium specimens from Near and Remote Oceania), 48 haplotypes are detected and haplotype cp-17 is predominant in both Near and Remote Oceania. Because cp-17 has an unambiguous Taiwanese origin and cp-17–carrying Oceanic paper mulberries are clonally propagated, our data concur with expectations of Taiwan as the Austronesian homeland, providing circumstantial support for the “out of Taiwan” hypothesis. Our data also provide insights into the dispersal of paper mulberry from South China “into North Taiwan,” the “out of South China–Indochina” expansion to New Guinea, and the geographic origins of post-European introductions of paper mulberry into Oceania. PMID:26438853
Matenaar, Daniela; Fingerle, Marcus; Heym, Eva; Wirtz, Sarah; Hochkirch, Axel
2018-01-01
Vicariance and dispersal are two important processes shaping biodiversity patterns. The South African Cape Floristic Region (CFR) is known for its high biotic diversity and endemism. However, studies on the phylogeography of endemic invertebrates in this biodiversity hotspot are still scarce. Here, we present a phylogenetic study of the flightless grasshopper genus Betiscoides, which is endemic to the CFR and strongly associated with restio plants (Restionaceae). We hypothesized that the genus originated in the southwestern part of the CFR, that differentiation within the genus is mainly an effect of vicariance and that the three known species only represent a minor fraction of the real genetic diversity of the genus. We inferred the phylogeny based on sequences of three mitochondrial and two nuclear genes from 99 Betiscoides specimens collected across the CFR. Furthermore, we conducted a SDIVA analysis to detect distributions of ancestral nodes and the possible spatial origin of these lineages. Strong differentiation among genetic lineages was shown. The ancestor of this genus was most likely distributed in the southwestern CFR. Five major lineages were detected, three of which were ancestrally distributed in the southwestern CFR. The ancestors of the two other lineages were distributed in the northern and eastern margins of the CFR. A total of 24 divergent evolutionary lineages were found, reflecting the geographical isolation of restio-dominated fynbos habitats. Dispersal played a more prominent role than expected in differentiation of Betiscoides. While the five main lineages were separated during a first phase via dispersal, differentiation occurred later and on smaller spatial scale, predominantly driven by isolation in montane refugia (i.e. vicariance). Our study also suggests that flightless insect taxa likely show high levels of differentiation in biodiversity hotspots with their taxonomy often being incomplete. Copyright © 2017 Elsevier Inc. All rights
Sympatric Asian felid phylogeography reveals a major Indochinese-Sundaic divergence.
Luo, Shu-Jin; Zhang, Yue; Johnson, Warren E; Miao, Lin; Martelli, Paolo; Antunes, Agostinho; Smith, James L D; O'Brien, Stephen J
2014-04-01
The dynamic geological and climatological history of Southeast Asia has spawned a complex array of ecosystems and 12 of the 37 known cat species, making it the most felid-rich region in the world. To examine the evolutionary histories of these poorly studied fauna, we compared phylogeography of six species (leopard cat Prionailurus bengalensis, fishing cat P. viverrinus, Asiatic golden cat Pardofelis temminckii, marbled cat P. marmorata, tiger Panthera tigris and leopard P. pardus) by sequencing over 5 kb of DNA each from 445 specimens at multiple loci of mtDNA, Y and X chromosomes. All species except the leopard displayed significant phylogenetic partitions between Indochina and Sundaland, with the central Thai-Malay Peninsula serving as the biogeographic boundary. Concordant mtDNA and nuclear DNA genealogies revealed deep Indochinese-Sundaic divergences around 2 MYA in both P. bengalensis and P. marmorata comparable to previously described interspecific distances within Felidae. The divergence coincided with serial sea level rises during the late Pliocene and early Pleistocene, and was probably reinforced by repeated isolation events associated with environmental changes throughout the Pleistocene. Indochinese-Sundaic differentiations within P. tigris and P. temminckii were more recent at 72-108 and 250-1570 kya, respectively. Overall, these results illuminate unexpected, deep vicariance events in Southeast Asian felids and provide compelling evidence of species-level distinction between the Indochinese and Sundaic populations in the leopard cat and marbled cat. Broader sampling and further molecular and morphometric analyses of these species will be instrumental in defining conservation units and effectively preserving Southeast Asian biodiversity. © 2014 John Wiley & Sons Ltd.
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Amy J Vogler
2017-09-01
Full Text Available Yersinia pestis appears to be maintained in multiple, geographically separate, and phylogenetically distinct subpopulations within the highlands of Madagascar. However, the dynamics of these locally differentiated subpopulations through time are mostly unknown. To address that gap and further inform our understanding of plague epidemiology, we investigated the phylogeography of Y. pestis in Madagascar over an 18 year period.We generated whole genome sequences for 31 strains and discovered new SNPs that we used in conjunction with previously identified SNPs and variable-number tandem repeats (VNTRs to genotype 773 Malagasy Y. pestis samples from 1995 to 2012. We mapped the locations where samples were obtained on a fine geographic scale to examine phylogeographic patterns through time. We identified 18 geographically separate and phylogenetically distinct subpopulations that display spatial and temporal stability, persisting in the same locations over a period of almost two decades. We found that geographic areas with higher levels of topographical relief are associated with greater levels of phylogenetic diversity and that sampling frequency can vary considerably among subpopulations and from year to year. We also found evidence of various Y. pestis dispersal events, including over long distances, but no evidence that any dispersal events resulted in successful establishment of a transferred genotype in a new location during the examined time period.Our analysis suggests that persistent endemic cycles of Y. pestis transmission within local areas are responsible for the long term maintenance of plague in Madagascar, rather than repeated episodes of wide scale epidemic spread. Landscape likely plays a role in maintaining Y. pestis subpopulations in Madagascar, with increased topographical relief associated with increased levels of localized differentiation. Local ecological factors likely affect the dynamics of individual subpopulations and the
Vogler, Amy J; Andrianaivoarimanana, Voahangy; Telfer, Sandra; Hall, Carina M; Sahl, Jason W; Hepp, Crystal M; Centner, Heather; Andersen, Genevieve; Birdsell, Dawn N; Rahalison, Lila; Nottingham, Roxanne; Keim, Paul; Wagner, David M; Rajerison, Minoarisoa
2017-09-01
Yersinia pestis appears to be maintained in multiple, geographically separate, and phylogenetically distinct subpopulations within the highlands of Madagascar. However, the dynamics of these locally differentiated subpopulations through time are mostly unknown. To address that gap and further inform our understanding of plague epidemiology, we investigated the phylogeography of Y. pestis in Madagascar over an 18 year period. We generated whole genome sequences for 31 strains and discovered new SNPs that we used in conjunction with previously identified SNPs and variable-number tandem repeats (VNTRs) to genotype 773 Malagasy Y. pestis samples from 1995 to 2012. We mapped the locations where samples were obtained on a fine geographic scale to examine phylogeographic patterns through time. We identified 18 geographically separate and phylogenetically distinct subpopulations that display spatial and temporal stability, persisting in the same locations over a period of almost two decades. We found that geographic areas with higher levels of topographical relief are associated with greater levels of phylogenetic diversity and that sampling frequency can vary considerably among subpopulations and from year to year. We also found evidence of various Y. pestis dispersal events, including over long distances, but no evidence that any dispersal events resulted in successful establishment of a transferred genotype in a new location during the examined time period. Our analysis suggests that persistent endemic cycles of Y. pestis transmission within local areas are responsible for the long term maintenance of plague in Madagascar, rather than repeated episodes of wide scale epidemic spread. Landscape likely plays a role in maintaining Y. pestis subpopulations in Madagascar, with increased topographical relief associated with increased levels of localized differentiation. Local ecological factors likely affect the dynamics of individual subpopulations and the associated
Phylogeography of a Morphologically Cryptic Golden Mole Assemblage from South-Eastern Africa.
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Samantha Mynhardt
Full Text Available The Greater Maputaland-Pondoland-Albany (GMPA region of southern Africa was recently designated as a centre of vertebrate endemism. The phylogeography of the vertebrate taxa occupying this region may provide insights into the evolution of faunal endemism in south-eastern Africa. Here we investigate the phylogeographic patterns of an understudied small mammal species assemblage (Amblysomus endemic to the GMPA, to test for cryptic diversity within the genus, and to better understand diversification across the region. We sampled specimens from 50 sites across the distributional range of Amblysomus, with emphasis on the widespread A. hottentotus, to analyse geographic patterns of genetic diversity using mitochondrial DNA (mtDNA and nuclear intron data. Molecular dating was used to elucidate the evolutionary and phylogeographic history of Amblysomus. Our phylogenetic reconstructions show that A. hottentotus comprises several distinct lineages, or evolutionarily significant units (ESUs, some with restricted geographic ranges and thus worthy of conservation attention. Divergence of the major lineages dated to the early Pliocene, with later radiations in the GMPA during the late-Pliocene to early-Pleistocene. Evolutionary diversification within Amblysomus may have been driven by uplift of the Great Escarpment c. 5-3 million years ago (Ma, habitat changes associated with intensification of the east-west rainfall gradient across South Africa and the influence of subsequent global climatic cycles. These drivers possibly facilitated geographic spread of ancestral lineages, local adaptation and vicariant isolation. Our study adds to growing empirical evidence identifying East and southern Africa as cradles of vertebrate diversity.
Collevatti, Rosane Garcia; Rabelo, Suelen Gonçalves; Vieira, Roberto F
2009-09-01
Lychnophora ericoides (Asteraceae) presents disjunct geographical distribution in cerrado rupestre in the south-east and central Brazil. The phylogeography of the species was investigated to understand the origin of the disjunct geographical distribution. Populations in the south and centre of Serra do Espinhaço, south-east Brazil and on ten other localities in Federal District and Goiás in central Brazil were sampled. Analyses were based on the polymorphisms at chloroplast (trnL intron and psbA-trnH intergenic spacer) and nuclear (ITS nrDNA) genomes. From 12 populations, 192 individuals were sequenced. Network analysis, AMOVA and the Mantel test were performed to understand the relationships among haplotypes and population genetic structure. To understand better the origin of disjunct distribution, demographic parameters and time to most recent common ancestor (T(MRCA)) were estimated using coalescent analyses. A remarkable differentiation between populations from the south-east and central Brazil was found and no haplotype was shared between these two regions. No significant effect of isolation by distance was detected. Coalescent analyses showed that some populations are shrinking and others are expanding and that gene flow between populations from the south-east and central Brazil was probably negligible. The results strongly support that the disjunct distribution of L. ericoides may represent a climatic relict and that long-distance gene flow is unlikely. With an estimated time to most recent common ancestor (T(MRCA)) dated from approx. 790,655 +/- 36,551 years bp (chloroplast) and approx. 623,555 +/- 55,769 years bp (ITS), it was hypothesized that the disjunct distribution may be a consequence of an expansion of the geographical distribution favoured by the drier and colder conditions that prevailed in much of Brazil during the Kansan glaciation, followed by the retraction of the distribution due to the extinction of populations in some areas as climate
Phylogeography and connectivity of molluscan parasites: Perkinsus spp. in Panama and beyond.
Pagenkopp Lohan, Katrina M; Hill-Spanik, Kristina M; Torchin, Mark E; Fleischer, Robert C; Carnegie, Ryan B; Reece, Kimberly S; Ruiz, Gregory M
2018-02-01
Panama is a major hub for commercial shipping between two oceans, making it an ideal location to examine parasite biogeography, potential invasions, and the spread of infectious agents. Our goals were to (i) characterise the diversity and genetic connectivity of Perkinsus spp. haplotypes across the Panamanian Isthmus and (ii) combine these data with sequences from around the world to evaluate the current phylogeography and genetic connectivity of these widespread molluscan parasites. We collected 752 bivalves from 12 locations along the coast of Panama including locations around the Bocas del Toro archipelago and the Caribbean and Pacific entrances to the Panama Canal, from December 2012 to February 2013. We used molecular genetic methods to screen for Perkinsus spp. and obtained internal transcribed spacer region (ITS) ribosomal DNA (rDNA) sequences for all positive samples. Our sequence data were used to evaluate regional haplotype diversity and distribution across both coasts of Panama, and were then combined with publicly available sequences to create global haplotype networks. We found 26 ITS haplotypes from four Perkinsus spp. (1-12 haplotypes per species) in Panama. Perkinsus beihaiensis haplotypes had the highest genetic diversity, were the most regionally widespread, and were associated with the greatest number of hosts. On a global scale, network analyses demonstrated that some haplotypes found in Panama were cosmopolitan (Perkinsus chesapeaki, Perkinsus marinus), while others were more geographically restricted (Perkinsus olseni, P. beihaiensis), indicating different levels of genetic connectivity and dispersal. We found some Perkinsus haplotypes were shared across the Isthmus of Panama and several regions around the world, including across ocean basins. We also found that haplotype diversity is currently underestimated and directly related to the number of sequences. Nevertheless, our results demonstrate long-range dispersal and global connectivity for
Zhang, Jinju; Li, Zuozhou; Fritsch, Peter W; Tian, Hua; Yang, Aihong; Yao, Xiaohong
2015-10-01
The phylogeography of plant species in sub-tropical China remains largely unclear. This study used Tapiscia sinensis, an endemic and endangered tree species widely but disjunctly distributed in sub-tropical China, as a model to reveal the patterns of genetic diversity and phylogeographical history of Tertiary relict plant species in this region. The implications of the results are discussed in relation to its conservation management. Samples were taken from 24 populations covering the natural geographical distribution of T. sinensis. Genetic structure was investigated by analysis of molecular variance (AMOVA) and spatial analysis of molecular variance (SAMOVA). Phylogenetic relationships among haplotypes were constructed with maximum parsimony and haplotype network methods. Historical population expansion events were tested with pairwise mismatch distribution analysis and neutrality tests. Species potential range was deduced by ecological niche modelling (ENM). A low level of genetic diversity was detected at the population level. A high level of genetic differentiation and a significant phylogeographical structure were revealed. The mean divergence time of the haplotypes was approx. 1·33 million years ago. Recent range expansion in this species is suggested by a star-like haplotype network and by the results from the mismatch distribution analysis and neutrality tests. Climatic oscillations during the Pleistocene have had pronounced effects on the extant distribution of Tapiscia relative to the Last Glacial Maximum (LGM). Spatial patterns of molecular variation and ENM suggest that T. sinensis may have retreated in south-western and central China and colonized eastern China prior to the LGM. Multiple montane refugia for T. sinense existing during the LGM are inferred in central and western China. The populations adjacent to or within these refugia of T. sinense should be given high priority in the development of conservation policies and management strategies for
Phylogeography and conservation genetics of the rare and relict Bretschneidera sinensis (Akaniaceae.
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Mei-Na Wang
Full Text Available Bretschneidera sinensis, a class-I protected wild plant in China, is a relic of the ancient Tertiary tropical flora endemic to Asia. However, little is known about its genetics and phylogeography. To elucidate the current phylogeographic patterns and infer the historical population dynamics of B. sinensis, and to make recommendations for its conservation, three non-coding regions of chloroplast DNA (trnQ-rps16, rps8-rps11, and trnT-trnL were amplified and sequenced across 256 individuals from 23 populations of B. sinensis, spanning 10 provinces of China. We recognized 13 haplotypes, demonstrating relatively high total haplotype diversity (hT = 0.739. Almost all of the variation existed among populations (98.09%, P < 0.001, but that within populations was low (1.91%, P < 0.001. Strong genetic differentiation was detected among populations (GST = 0.855, P < 0.001 with limited estimations of seed flow (Nm = 0.09, indicating that populations were strongly isolated from one another. According to SAMOVA analysis, populations of B. sinensis in China could be divided into five geographic groups: (1 eastern Yunnan to western Guangxi; (2 Guizhou-Hunan-Hubei; (3 central Guangdong; (4 northwestern Guangdong; and (5 the Luoxiao-Nanling-Wuyi -Yangming Mountain. Network analysis showed that the most ancestral haplotypes were located in the first group, i.e., the eastern Yungui Plateau and in eastern Yunnan, which is regarded as a putative glacial refugia for B. sinensis in China. B. sinensis may have expanded its range eastward from these refugia and experienced bottleneck or founder effects in southeastern China. Populations in Liping (Guizhou Province, Longsheng (Guangxi Province, Huizhou (Guangdong Province, Chongyi (Jiangxi Province, Dong-an (Hunan Province, Pingbian (Yunnan Province and Xinning (Hunan Province are proposed as the priority protection units.
Brase, Gary L; Hill, W Trey
2015-01-01
Bayesian reasoning, defined here as the updating of a posterior probability following new information, has historically been problematic for humans. Classic psychology experiments have tested human Bayesian reasoning through the use of word problems and have evaluated each participant's performance against the normatively correct answer provided by Bayes' theorem. The standard finding is of generally poor performance. Over the past two decades, though, progress has been made on how to improve Bayesian reasoning. Most notably, research has demonstrated that the use of frequencies in a natural sampling framework-as opposed to single-event probabilities-can improve participants' Bayesian estimates. Furthermore, pictorial aids and certain individual difference factors also can play significant roles in Bayesian reasoning success. The mechanics of how to build tasks which show these improvements is not under much debate. The explanations for why naturally sampled frequencies and pictures help Bayesian reasoning remain hotly contested, however, with many researchers falling into ingrained "camps" organized around two dominant theoretical perspectives. The present paper evaluates the merits of these theoretical perspectives, including the weight of empirical evidence, theoretical coherence, and predictive power. By these criteria, the ecological rationality approach is clearly better than the heuristics and biases view. Progress in the study of Bayesian reasoning will depend on continued research that honestly, vigorously, and consistently engages across these different theoretical accounts rather than staying "siloed" within one particular perspective. The process of science requires an understanding of competing points of view, with the ultimate goal being integration.
MATHEMATICAL RISK ANALYSIS: VIA NICHOLAS RISK MODEL AND BAYESIAN ANALYSIS
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Anass BAYAGA
2010-07-01
Full Text Available The objective of this second part of a two-phased study was to explorethe predictive power of quantitative risk analysis (QRA method andprocess within Higher Education Institution (HEI. The method and process investigated the use impact analysis via Nicholas risk model and Bayesian analysis, with a sample of hundred (100 risk analysts in a historically black South African University in the greater Eastern Cape Province.The first findings supported and confirmed previous literature (KingIII report, 2009: Nicholas and Steyn, 2008: Stoney, 2007: COSA, 2004 that there was a direct relationship between risk factor, its likelihood and impact, certiris paribus. The second finding in relation to either controlling the likelihood or the impact of occurrence of risk (Nicholas risk model was that to have a brighter risk reward, it was important to control the likelihood ofoccurrence of risks as compared with its impact so to have a direct effect on entire University. On the Bayesian analysis, thus third finding, the impact of risk should be predicted along three aspects. These aspects included the human impact (decisions made, the property impact (students and infrastructural based and the business impact. Lastly, the study revealed that although in most business cases, where as business cycles considerably vary dependingon the industry and or the institution, this study revealed that, most impacts in HEI (University was within the period of one academic.The recommendation was that application of quantitative risk analysisshould be related to current legislative framework that affects HEI.
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Kuntner Matjaž
2011-05-01
Full Text Available Abstract Background The origin and diversification patterns of lineages across the Indian Ocean islands are varied due to the interplay of the complex geographic and geologic island histories, the varying dispersal abilities of biotas, and the proximity to major continental landmasses. Our aim was to reconstruct phylogeographic history of the giant orbweaving spider (Nephila on western Indian Ocean islands (Madagascar, Mayotte, Réunion, Mauritius, Rodrigues, to test its origin and route of dispersal, and to examine the consequences of good dispersal abilities for colonization and diversification, in comparison with related spiders (Nephilengys inhabiting the same islands, and with other organisms known for over water dispersal. We used mitochondrial (COI and nuclear (ITS2 markers to examine phylogenetic and population genetic patterns in Nephila populations and species. We employed Bayesian and parsimony methods to reconstruct phylogenies and haplotype networks, respectively, and calculated genetic distances, fixation indices, and estimated clade ages under a relaxed clock model. Results Our results suggest an African origin of Madagascar Nephila inaurata populations via Cenozoic dispersal, and the colonization of the Mascarene islands from Madagascar. We find evidence of gene flow across Madagascar and Comoros. The Mascarene islands share a common 'ancestral' COI haplotype closely related to those found on Madagascar, but itself absent, or as yet unsampled, from Madagascar. Each island has one or more unique haplotypes related to the ancestral Mascarene haplotype. The Indian Ocean N. inaurata are genetically distinct from the African populations. Conclusions Nephila spiders colonized Madagascar from Africa about 2.5 (0.6-5.3 Ma. Our results are consistent with subsequent, recent and rapid, colonization of all three Mascarene islands. On each island, however, we detected unique haplotypes, consistent with a limited gene flow among the islands
Bayesian analogy with relational transformations.
Lu, Hongjing; Chen, Dawn; Holyoak, Keith J
2012-07-01
How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuous-valued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought. Copyright 2012 APA, all rights reserved.
Bayesian tomographic reconstruction of microsystems
International Nuclear Information System (INIS)
Salem, Sofia Fekih; Vabre, Alexandre; Mohammad-Djafari, Ali
2007-01-01
The microtomography by X ray transmission plays an increasingly dominating role in the study and the understanding of microsystems. Within this framework, an experimental setup of high resolution X ray microtomography was developed at CEA-List to quantify the physical parameters related to the fluids flow in microsystems. Several difficulties rise from the nature of experimental data collected on this setup: enhanced error measurements due to various physical phenomena occurring during the image formation (diffusion, beam hardening), and specificities of the setup (limited angle, partial view of the object, weak contrast).To reconstruct the object we must solve an inverse problem. This inverse problem is known to be ill-posed. It therefore needs to be regularized by introducing prior information. The main prior information we account for is that the object is composed of a finite known number of different materials distributed in compact regions. This a priori information is introduced via a Gauss-Markov field for the contrast distributions with a hidden Potts-Markov field for the class materials in the Bayesian estimation framework. The computations are done by using an appropriate Markov Chain Monte Carlo (MCMC) technique.In this paper, we present first the basic steps of the proposed algorithms. Then we focus on one of the main steps in any iterative reconstruction method which is the computation of forward and adjoint operators (projection and backprojection). A fast implementation of these two operators is crucial for the real application of the method. We give some details on the fast computation of these steps and show some preliminary results of simulations
Objective Bayesianism and the Maximum Entropy Principle
Directory of Open Access Journals (Sweden)
Jon Williamson
2013-09-01
Full Text Available Objective Bayesian epistemology invokes three norms: the strengths of our beliefs should be probabilities; they should be calibrated to our evidence of physical probabilities; and they should otherwise equivocate sufficiently between the basic propositions that we can express. The three norms are sometimes explicated by appealing to the maximum entropy principle, which says that a belief function should be a probability function, from all those that are calibrated to evidence, that has maximum entropy. However, the three norms of objective Bayesianism are usually justified in different ways. In this paper, we show that the three norms can all be subsumed under a single justification in terms of minimising worst-case expected loss. This, in turn, is equivalent to maximising a generalised notion of entropy. We suggest that requiring language invariance, in addition to minimising worst-case expected loss, motivates maximisation of standard entropy as opposed to maximisation of other instances of generalised entropy. Our argument also provides a qualified justification for updating degrees of belief by Bayesian conditionalisation. However, conditional probabilities play a less central part in the objective Bayesian account than they do under the subjective view of Bayesianism, leading to a reduced role for Bayes’ Theorem.
A default Bayesian hypothesis test for mediation.
Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan
2015-03-01
In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).
Classifying emotion in Twitter using Bayesian network
Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya
2018-03-01
Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.
Takada, Yoshitake; Sakuma, Kay; Fujii, Tetsuo; Kojima, Shigeaki
2018-01-01
Recent findings of genetic breaks within apparently continuous marine populations challenge the traditional vicariance paradigm in population genetics. Such "invisible" boundaries are sometimes associated with potential geographic barriers that have forced divergence of an ancestral population, habitat discontinuities, biogeographic disjunctions due to environmental gradients, or a combination of these factors. To explore the factors that influence the genetic population structure of apparently continuous populations along the Sea of Japan, the sandy beach amphipod Haustorioides japonicus was examined. We sampled a total of 300 individuals of H. japonicus from the coast of Japan, and obtained partial sequences of the mitochondrial COI gene. The sequences from 19 local populations were clustered into five groups (Northwestern Pacific, Northern, Central, Southern Sea of Japan, and East China Sea) based on a spatial genetic mixture analysis and a minimum-spanning network. AMOVA and pairwise Fst tests further supported the significant divergence of the five groups. Phylogenetic analysis revealed the relationship among the haplotypes of H. japonicus and outgroups, which inferred the northward range expansion of the species. A relaxed molecular-clock Bayesian analysis inferred the early-to middle-Pleistocene divergence of the populations. Among the five clusters, the Central Sea of Japan showed the highest values for genetic diversity indices indicating the existence of a relatively stable and large population there. The hypothesis is also supported by Bayesian Skyline Plots that showed sudden population expansion for all the clusters except for Central Sea of Japan. The present study shows genetic boundaries between the Sea of Japan and the neighboring seas, probably due to geographic isolation during the Pleistocene glacial periods. We further found divergence between the populations along the apparently continuous coast of the Sea of Japan. Historical changes in the
Parameterizing Bayesian network Representations of Social-Behavioral Models by Expert Elicitation
Energy Technology Data Exchange (ETDEWEB)
Walsh, Stephen J.; Dalton, Angela C.; Whitney, Paul D.; White, Amanda M.
2010-05-23
Bayesian networks provide a general framework with which to model many natural phenomena. The mathematical nature of Bayesian networks enables a plethora of model validation and calibration techniques: e.g parameter estimation, goodness of fit tests, and diagnostic checking of the model assumptions. However, they are not free of shortcomings. Parameter estimation from relevant extant data is a common approach to calibrating the model parameters. In practice it is not uncommon to find oneself lacking adequate data to reliably estimate all model parameters. In this paper we present the early development of a novel application of conjoint analysis as a method for eliciting and modeling expert opinions and using the results in a methodology for calibrating the parameters of a Bayesian network.
Filippi, Sarah; Holmes, Chris C; Nieto-Barajas, Luis E
2016-11-16
In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a "null model" of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets.
How few countries will do? Comparative survey analysis from a Bayesian perspective
Directory of Open Access Journals (Sweden)
Joop J.C.M. Hox
2012-07-01
Full Text Available Meuleman and Billiet (2009 have carried out a simulation study aimed at the question how many countries are needed for accurate multilevel SEM estimation in comparative studies. The authors concluded that a sample of 50 to 100 countries is needed for accurate estimation. Recently, Bayesian estimation methods have been introduced in structural equation modeling which should work well with much lower sample sizes. The current study reanalyzes the simulation of Meuleman and Billiet using Bayesian estimation to find the lowest number of countries needed when conducting multilevel SEM. The main result of our simulations is that a sample of about 20 countries is sufficient for accurate Bayesian estimation, which makes multilevel SEM practicable for the number of countries commonly available in large scale comparative surveys.
Empirical Bayesian inference and model uncertainty
International Nuclear Information System (INIS)
Poern, K.
1994-01-01
This paper presents a hierarchical or multistage empirical Bayesian approach for the estimation of uncertainty concerning the intensity of a homogeneous Poisson process. A class of contaminated gamma distributions is considered to describe the uncertainty concerning the intensity. These distributions in turn are defined through a set of secondary parameters, the knowledge of which is also described and updated via Bayes formula. This two-stage Bayesian approach is an example where the modeling uncertainty is treated in a comprehensive way. Each contaminated gamma distributions, represented by a point in the 3D space of secondary parameters, can be considered as a specific model of the uncertainty about the Poisson intensity. Then, by the empirical Bayesian method each individual model is assigned a posterior probability
Bayesian Methods for Radiation Detection and Dosimetry
Groer, Peter G
2002-01-01
We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed comp...
Bayesian estimation of dose rate effectiveness
International Nuclear Information System (INIS)
Arnish, J.J.; Groer, P.G.
2000-01-01
A Bayesian statistical method was used to quantify the effectiveness of high dose rate 137 Cs gamma radiation at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice. The Bayesian approach considers both the temporal and dose dependence of radiation carcinogenesis and total mortality. This paper provides the first direct estimation of dose rate effectiveness using Bayesian statistics. This statistical approach provides a quantitative description of the uncertainty of the factor characterising the dose rate in terms of a probability density function. The results show that a fixed dose from 137 Cs gamma radiation delivered at a high dose rate is more effective at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice than the same dose delivered at a low dose rate. (author)
Evolutionary History and Phylogeography of Rabies Viruses Associated with Outbreaks in Trinidad
Seetahal, Janine F. R.; Velasco-Villa, Andres; Allicock, Orchid M.; Adesiyun, Abiodun A.; Bissessar, Joseph; Amour, Kirk; Phillip-Hosein, Annmarie; Marston, Denise A.; McElhinney, Lorraine M.; Shi, Mang; Wharwood, Cheryl-Ann; Fooks, Anthony R.; Carrington, Christine V. F.
2013-01-01
Bat rabies is an emerging disease of public health significance in the Americas. The Caribbean island of Trinidad experiences periodic outbreaks within the livestock population. We performed molecular characterisation of Trinidad rabies virus (RABV) and used a Bayesian phylogeographic approach to investigate the extent to which outbreaks are a result of in situ evolution versus importation of virus from the nearby South American mainland. Trinidadian RABV sequences were confirmed as bat varia...
A nonparametric Bayesian approach for genetic evaluation in ...
African Journals Online (AJOL)
South African Journal of Animal Science ... the Bayesian and Classical models, a Bayesian procedure is provided which allows these random ... data from the Elsenburg Dormer sheep stud and data from a simulation experiment are utilized. >
Bayesian disease mapping: hierarchical modeling in spatial epidemiology
National Research Council Canada - National Science Library
Lawson, Andrew
2013-01-01
.... Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications...
Sparse reconstruction using distribution agnostic bayesian matching pursuit
Masood, Mudassir; Al-Naffouri, Tareq Y.
2013-01-01
A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics
Sironi, Emanuele; Taroni, Franco; Baldinotti, Claudio; Nardi, Cosimo; Norelli, Gian-Aristide; Gallidabino, Matteo; Pinchi, Vilma
2017-11-14
The present study aimed to investigate the performance of a Bayesian method in the evaluation of dental age-related evidence collected by means of a geometrical approximation procedure of the pulp chamber volume. Measurement of this volume was based on three-dimensional cone beam computed tomography images. The Bayesian method was applied by means of a probabilistic graphical model, namely a Bayesian network. Performance of that method was investigated in terms of accuracy and bias of the decisional outcomes. Influence of an informed elicitation of the prior belief of chronological age was also studied by means of a sensitivity analysis. Outcomes in terms of accuracy were adequate with standard requirements for forensic adult age estimation. Findings also indicated that the Bayesian method does not show a particular tendency towards under- or overestimation of the age variable. Outcomes of the sensitivity analysis showed that results on estimation are improved with a ration elicitation of the prior probabilities of age.
Bayesian analysis of deterministic and stochastic prisoner's dilemma games
Directory of Open Access Journals (Sweden)
Howard Kunreuther
2009-08-01
Full Text Available This paper compares the behavior of individuals playing a classic two-person deterministic prisoner's dilemma (PD game with choice data obtained from repeated interdependent security prisoner's dilemma games with varying probabilities of loss and the ability to learn (or not learn about the actions of one's counterpart, an area of recent interest in experimental economics. This novel data set, from a series of controlled laboratory experiments, is analyzed using Bayesian hierarchical methods, the first application of such methods in this research domain. We find that individuals are much more likely to be cooperative when payoffs are deterministic than when the outcomes are probabilistic. A key factor explaining this difference is that subjects in a stochastic PD game respond not just to what their counterparts did but also to whether or not they suffered a loss. These findings are interpreted in the context of behavioral theories of commitment, altruism and reciprocity. The work provides a linkage between Bayesian statistics, experimental economics, and consumer psychology.
Bayesian uncertainty analyses of probabilistic risk models
International Nuclear Information System (INIS)
Pulkkinen, U.
1989-01-01
Applications of Bayesian principles to the uncertainty analyses are discussed in the paper. A short review of the most important uncertainties and their causes is provided. An application of the principle of maximum entropy to the determination of Bayesian prior distributions is described. An approach based on so called probabilistic structures is presented in order to develop a method of quantitative evaluation of modelling uncertainties. The method is applied to a small example case. Ideas for application areas for the proposed method are discussed
Justifying Objective Bayesianism on Predicate Languages
Directory of Open Access Journals (Sweden)
Jürgen Landes
2015-04-01
Full Text Available Objective Bayesianism says that the strengths of one’s beliefs ought to be probabilities, calibrated to physical probabilities insofar as one has evidence of them, and otherwise sufficiently equivocal. These norms of belief are often explicated using the maximum entropy principle. In this paper we investigate the extent to which one can provide a unified justification of the objective Bayesian norms in the case in which the background language is a first-order predicate language, with a view to applying the resulting formalism to inductive logic. We show that the maximum entropy principle can be motivated largely in terms of minimising worst-case expected loss.
Motion Learning Based on Bayesian Program Learning
Directory of Open Access Journals (Sweden)
Cheng Meng-Zhen
2017-01-01
Full Text Available The concept of virtual human has been highly anticipated since the 1980s. By using computer technology, Human motion simulation could generate authentic visual effect, which could cheat human eyes visually. Bayesian Program Learning train one or few motion data, generate new motion data by decomposing and combining. And the generated motion will be more realistic and natural than the traditional one.In this paper, Motion learning based on Bayesian program learning allows us to quickly generate new motion data, reduce workload, improve work efficiency, reduce the cost of motion capture, and improve the reusability of data.
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...
Length Scales in Bayesian Automatic Adaptive Quadrature
Directory of Open Access Journals (Sweden)
Adam Gh.
2016-01-01
Full Text Available Two conceptual developments in the Bayesian automatic adaptive quadrature approach to the numerical solution of one-dimensional Riemann integrals [Gh. Adam, S. Adam, Springer LNCS 7125, 1–16 (2012] are reported. First, it is shown that the numerical quadrature which avoids the overcomputing and minimizes the hidden floating point loss of precision asks for the consideration of three classes of integration domain lengths endowed with specific quadrature sums: microscopic (trapezoidal rule, mesoscopic (Simpson rule, and macroscopic (quadrature sums of high algebraic degrees of precision. Second, sensitive diagnostic tools for the Bayesian inference on macroscopic ranges, coming from the use of Clenshaw-Curtis quadrature, are derived.
Bayesian parameter estimation in probabilistic risk assessment
International Nuclear Information System (INIS)
Siu, Nathan O.; Kelly, Dana L.
1998-01-01
Bayesian statistical methods are widely used in probabilistic risk assessment (PRA) because of their ability to provide useful estimates of model parameters when data are sparse and because the subjective probability framework, from which these methods are derived, is a natural framework to address the decision problems motivating PRA. This paper presents a tutorial on Bayesian parameter estimation especially relevant to PRA. It summarizes the philosophy behind these methods, approaches for constructing likelihood functions and prior distributions, some simple but realistic examples, and a variety of cautions and lessons regarding practical applications. References are also provided for more in-depth coverage of various topics
Bayesian estimation and tracking a practical guide
Haug, Anton J
2012-01-01
A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation
Quantum Bayesian networks with application to games displaying Parrondo's paradox
Pejic, Michael
Bayesian networks and their accompanying graphical models are widely used for prediction and analysis across many disciplines. We will reformulate these in terms of linear maps. This reformulation will suggest a natural extension, which we will show is equivalent to standard textbook quantum mechanics. Therefore, this extension will be termed quantum. However, the term quantum should not be taken to imply this extension is necessarily only of utility in situations traditionally thought of as in the domain of quantum mechanics. In principle, it may be employed in any modelling situation, say forecasting the weather or the stock market---it is up to experiment to determine if this extension is useful in practice. Even restricting to the domain of quantum mechanics, with this new formulation the advantages of Bayesian networks can be maintained for models incorporating quantum and mixed classical-quantum behavior. The use of these will be illustrated by various basic examples. Parrondo's paradox refers to the situation where two, multi-round games with a fixed winning criteria, both with probability greater than one-half for one player to win, are combined. Using a possibly biased coin to determine the rule to employ for each round, paradoxically, the previously losing player now wins the combined game with probabilitygreater than one-half. Using the extended Bayesian networks, we will formulate and analyze classical observed, classical hidden, and quantum versions of a game that displays this paradox, finding bounds for the discrepancy from naive expectations for the occurrence of the paradox. A quantum paradox inspired by Parrondo's paradox will also be analyzed. We will prove a bound for the discrepancy from naive expectations for this paradox as well. Games involving quantum walks that achieve this bound will be presented.
ABCtoolbox: a versatile toolkit for approximate Bayesian computations
Directory of Open Access Journals (Sweden)
Neuenschwander Samuel
2010-03-01
Full Text Available Abstract Background The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. Results Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC. It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABCtoolbox can also interact with most simulation and summary statistics computation programs. The usability of the ABCtoolbox is demonstrated by inferring the evolutionary history of two evolutionary lineages of Microtus arvalis. Using nuclear microsatellites and mitochondrial sequence data in the same estimation procedure enabled us to infer sex-specific population sizes and migration rates and to find that males show smaller population sizes but much higher levels of migration than females. Conclusion ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results.
A Fast Iterative Bayesian Inference Algorithm for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand; Manchón, Carles Navarro; Fleury, Bernard Henri
2013-01-01
representation of the Bessel K probability density function; a highly efficient, fast iterative Bayesian inference method is then applied to the proposed model. The resulting estimator outperforms other state-of-the-art Bayesian and non-Bayesian estimators, either by yielding lower mean squared estimation error...
A Gentle Introduction to Bayesian Analysis : Applications to Developmental Research
Van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A G
2014-01-01
Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,
A gentle introduction to Bayesian analysis : Applications to developmental research
van de Schoot, R.; Kaplan, D.; Denissen, J.J.A.; Asendorpf, J.B.; Neyer, F.J.; van Aken, M.A.G.
2014-01-01
Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,
A default Bayesian hypothesis test for ANOVA designs
Wetzels, R.; Grasman, R.P.P.P.; Wagenmakers, E.J.
2012-01-01
This article presents a Bayesian hypothesis test for analysis of variance (ANOVA) designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA
Prior approval: the growth of Bayesian methods in psychology.
Andrews, Mark; Baguley, Thom
2013-02-01
Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.
Directory of Open Access Journals (Sweden)
Pierce Naomi E
2008-10-01
Full Text Available Abstract Background Evolutionary genetics provides a rich theoretical framework for empirical studies of phylogeography. Investigations of intraspecific genetic variation can uncover new putative species while allowing inference into the evolutionary origin and history of extant populations. With a distribution on four continents ranging throughout most of the Old World, Lampides boeticus (Lepidoptera: Lycaenidae is one of the most widely distributed species of butterfly. It is placed in a monotypic genus with no commonly accepted subspecies. Here, we investigate the demographic history and taxonomic status of this widespread species, and screen for the presence or absence of the bacterial endosymbiont Wolbachia. Results We performed phylogenetic, population genetic, and phylogeographic analyses using 1799 bp of mitochondrial sequence data from 57 specimens collected throughout the species' range. Most of the samples (>90% were nearly genetically identical, with uncorrected pairwise sequence differences of 0 – 0.5% across geographic distances > 9,000 km. However, five samples from central Thailand, Madagascar, northern Australia and the Moluccas formed two divergent clades differing from the majority of samples by uncorrected pairwise distances ranging from 1.79 – 2.21%. Phylogenetic analyses suggest that L. boeticus is almost certainly monophyletic, with all sampled genes coalescing well after the divergence from three closely related taxa included for outgroup comparisons. Analyses of molecular diversity indicate that most L. boeticus individuals in extant populations are descended from one or two relatively recent population bottlenecks. Conclusion The combined analyses suggest a scenario in which the most recent common ancestor of L. boeticus and its sister taxon lived in the African region approximately 7 Mya; extant lineages of L. boeticus began spreading throughout the Old World at least 1.5 Mya. More recently, expansion after
Waldrop, Ellen
2016-01-11
Aim This study compares the phylogeography, population structure and evolution of four butterflyfish species in the Chaetodon subgenus Corallochaetodon, with two widespread species (Indian Ocean – C. trifasciatus and Pacific Ocean – C. lunulatus), and two species that are largely restricted to the Red Sea (C. austriacus) and north-western (NW) Indian Ocean (C. melapterus). Through extensive geographical coverage of these taxa, we seek to resolve patterns of genetic diversity within and between closely related butterflyfish species in order to illuminate biogeographical and evolutionary processes. Location Red Sea, Indian Ocean and Pacific Ocean. Methods A total of 632 individuals from 24 locations throughout the geographical ranges of all four members of the subgenus Corallochaetodon were sequenced using a 605 bp fragment (cytochrome b) of mtDNA. In addition, 10 microsatellite loci were used to assess population structure in the two widespread species. Results Phylogenetic reconstruction indicates that the Pacific Ocean C. lunulatus diverged from the Indian Ocean C. trifasciatus approximately 3 Ma, while C. melapterus and C. austriacus comprise a cluster of shared haplotypes derived from C. trifasciatus within the last 0.75 Myr. The Pacific C. lunulatus had significant population structure at peripheral locations on the eastern edge of its range (French Polynesia, Johnston Atoll, Hawai\\'i), and a strong break between two ecoregions of the Hawaiian Archipelago. The Indian Ocean C. trifasciatus showed significant structure only at the Chagos Archipelago in the central Indian Ocean, and the two range-restricted species showed no population structure but evidence of recent population expansion. Main conclusions Patterns of endemism and genetic diversity in Corallochaetodon butterflyfishes have been shaped by (1) Plio-Pleistocene sea level changes that facilitated evolutionary divergences at biogeographical barriers between Indian and Pacific Oceans, and the Indian
Directory of Open Access Journals (Sweden)
Li Shouhsien
2009-06-01
Full Text Available Abstract Background The role of Pleistocene glacial oscillations in current biodiversity and distribution patterns varies with latitude, physical topology and population life history and has long been a topic of discussion. However, there had been little phylogeographical research in south China, where the geophysical complexity is associated with great biodiversity. A bird endemic in Southeast Asia, the Grey-cheeked Fulvetta, Alcippe morrisonia, has been reported to show deep genetic divergences among its seven subspecies. In the present study, we investigated the phylogeography of A. morrisonia to explore its population structure and evolutionary history, in order to gain insight into the effect of geological events on the speciation and diversity of birds endemic in south China. Results Mitochondrial genes cytochrome b (Cytb and cytochrome c oxidase I (COI were represented by 1236 nucleotide sites from 151 individuals from 29 localities. Phylogenetic analysis showed seven monophyletic clades congruent with the geographically separated groups, which were identified as major sources of molecular variance (90.92% by AMOVA. TCS analysis revealed four disconnected networks, and that no haplotype was shared among the geographical groups. The common ancestor of these populations was dated to 11.6 Mya and several divergence events were estimated along the population evolutionary history. Isolation by distance was inferred by NCPA to be responsible for the current intra-population genetic pattern and gene flow among geographical groups was interrupted. A late Pleistocene demographic expansion was detected in the eastern geographical groups, while the expansion time (0.2–0.4 Mya was earlier than the Last Glacial Maximum. Conclusion It is proposed that the complicated topology preserves high genetic diversity and ancient lineages for geographical groups of A. morrisonia in China mainland and its two major islands, and restricts gene exchange during
Phylogeography of the asian elephant (Elephas maximus) based on mitochondrial DNA.
Fleischer, R C; Perry, E A; Muralidharan, K; Stevens, E E; Wemmer, C M
2001-09-01
Populations of the Asian elephant (Elephas maximus) have been reduced in size and become highly fragmented during the past 3,000 to 4,000 years. Historical records reveal elephant dispersal by humans via trade and war. How have these anthropogenic impacts affected genetic variation and structure of Asian elephant populations? We sequenced mitochondrial DNA (mtDNA) to assay genetic variation and phylogeography across much of the Asian elephant's range. Initially we compare cytochrome b sequences (cyt b) between nine Asian and five African elephants and use the fossil-based age of their separation (approximately 5 million years ago) to obtain a rate of about 0.013 (95% CI = 0.011-0.018) corrected sequence divergence per million years. We also assess variation in part of the mtDNA control region (CR) and adjacent tRNA genes in 57 Asian elephants from seven countries (Sri Lanka, India, Nepal, Myanmar, Thailand, Malaysia, and Indonesia). Asian elephants have typical levels of mtDNA variation, and coalescence analyses suggest their populations were growing in the late Pleistocene. Reconstructed phylogenies reveal two major clades (A and B) differing on average by HKY85/gamma-corrected distances of 0.020 for cyt b and 0.050 for the CR segment (corresponding to a coalescence time based on our cyt b rate of approximately 1.2 million years). Individuals of both major clades exist in all locations but Indonesia and Malaysia. Most elephants from Malaysia and all from Indonesia are in well-supported, basal clades within clade A. thus supporting their status as evolutionarily significant units (ESUs). The proportion of clade A individuals decreases to the north, which could result from retention and subsequent loss of ancient lineages in long-term stable populations or, perhaps more likely, via recent mixing of two expanding populations that were isolated in the mid-Pleistocene. The distribution of clade A individuals appears to have been impacted by human trade in elephants
Directory of Open Access Journals (Sweden)
Sebastián Poljak
Full Text Available Little is known about phylogeography of armadillo species native to southern South America. In this study we describe the phylogeography of the screaming hairy armadillo Chaetophractus vellerosus, discuss previous hypothesis about the origin of its disjunct distribution and propose an alternative one, based on novel information on genetic variability. Variation of partial sequences of mitochondrial DNA Control Region (CR from 73 individuals from 23 localities were analyzed to carry out a phylogeographic analysis using neutrality tests, mismatch distribution, median-joining (MJ network and paleontological records. We found 17 polymorphic sites resulting in 15 haplotypes. Two new geographic records that expand known distribution of the species are presented; one of them links the distributions of recently synonimized species C. nationi and C. vellerosus. Screaming hairy armadillo phylogeographic pattern can be addressed as category V of Avise: common widespread linages plus closely related lineages confined to one or a few nearby locales each. The older linages are distributed in the north-central area of the species distribution range in Argentina (i.e. ancestral area of distribution. C. vellerosus seems to be a low vagility species that expanded, and probably is expanding, its distribution range while presents signs of genetic structuring in central areas. To explain the disjunct distribution, a hypothesis of extinction of the species in intermediate areas due to quaternary climatic shift to more humid conditions was proposed. We offer an alternative explanation: long distance colonization, based on null genetic variability, paleontological record and evidence of alternance of cold/arid and temperate/humid climatic periods during the last million years in southern South America.
Poljak, Sebastián; Ferreiro, Alejandro M; Chiappero, Marina B; Sánchez, Julieta; Gabrielli, Magalí; Lizarralde, Marta S
2018-01-01
Little is known about phylogeography of armadillo species native to southern South America. In this study we describe the phylogeography of the screaming hairy armadillo Chaetophractus vellerosus, discuss previous hypothesis about the origin of its disjunct distribution and propose an alternative one, based on novel information on genetic variability. Variation of partial sequences of mitochondrial DNA Control Region (CR) from 73 individuals from 23 localities were analyzed to carry out a phylogeographic analysis using neutrality tests, mismatch distribution, median-joining (MJ) network and paleontological records. We found 17 polymorphic sites resulting in 15 haplotypes. Two new geographic records that expand known distribution of the species are presented; one of them links the distributions of recently synonimized species C. nationi and C. vellerosus. Screaming hairy armadillo phylogeographic pattern can be addressed as category V of Avise: common widespread linages plus closely related lineages confined to one or a few nearby locales each. The older linages are distributed in the north-central area of the species distribution range in Argentina (i.e. ancestral area of distribution). C. vellerosus seems to be a low vagility species that expanded, and probably is expanding, its distribution range while presents signs of genetic structuring in central areas. To explain the disjunct distribution, a hypothesis of extinction of the species in intermediate areas due to quaternary climatic shift to more humid conditions was proposed. We offer an alternative explanation: long distance colonization, based on null genetic variability, paleontological record and evidence of alternance of cold/arid and temperate/humid climatic periods during the last million years in southern South America.
Bayesian Meta-Analysis of Coefficient Alpha
Brannick, Michael T.; Zhang, Nanhua
2013-01-01
The current paper describes and illustrates a Bayesian approach to the meta-analysis of coefficient alpha. Alpha is the most commonly used estimate of the reliability or consistency (freedom from measurement error) for educational and psychological measures. The conventional approach to meta-analysis uses inverse variance weights to combine…
Bayesian decision theory : A simple toy problem
van Erp, H.R.N.; Linger, R.O.; van Gelder, P.H.A.J.M.
2016-01-01
We give here a comparison of the expected outcome theory, the expected utility theory, and the Bayesian decision theory, by way of a simple numerical toy problem in which we look at the investment willingness to avert a high impact low probability event. It will be found that for this toy problem
Heuristics as Bayesian inference under extreme priors.
Parpart, Paula; Jones, Matt; Love, Bradley C
2018-05-01
Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
A strongly quasiconvex PAC-Bayesian bound
DEFF Research Database (Denmark)
Thiemann, Niklas; Igel, Christian; Wintenberger, Olivier
2017-01-01
We propose a new PAC-Bayesian bound and a way of constructing a hypothesis space, so that the bound is convex in the posterior distribution and also convex in a trade-off parameter between empirical performance of the posterior distribution and its complexity. The complexity is measured by the Ku...
Multisnapshot Sparse Bayesian Learning for DOA
DEFF Research Database (Denmark)
Gerstoft, Peter; Mecklenbrauker, Christoph F.; Xenaki, Angeliki
2016-01-01
The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source...
Approximate Bayesian evaluations of measurement uncertainty
Possolo, Antonio; Bodnar, Olha
2018-04-01
The Guide to the Expression of Uncertainty in Measurement (GUM) includes formulas that produce an estimate of a scalar output quantity that is a function of several input quantities, and an approximate evaluation of the associated standard uncertainty. This contribution presents approximate, Bayesian counterparts of those formulas for the case where the output quantity is a parameter of the joint probability distribution of the input quantities, also taking into account any information about the value of the output quantity available prior to measurement expressed in the form of a probability distribution on the set of possible values for the measurand. The approximate Bayesian estimates and uncertainty evaluations that we present have a long history and illustrious pedigree, and provide sufficiently accurate approximations in many applications, yet are very easy to implement in practice. Differently from exact Bayesian estimates, which involve either (analytical or numerical) integrations, or Markov Chain Monte Carlo sampling, the approximations that we describe involve only numerical optimization and simple algebra. Therefore, they make Bayesian methods widely accessible to metrologists. We illustrate the application of the proposed techniques in several instances of measurement: isotopic ratio of silver in a commercial silver nitrate; odds of cryptosporidiosis in AIDS patients; height of a manometer column; mass fraction of chromium in a reference material; and potential-difference in a Zener voltage standard.
Inverse Problems in a Bayesian Setting
Matthies, Hermann G.
2016-02-13
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.
Error probabilities in default Bayesian hypothesis testing
Gu, Xin; Hoijtink, Herbert; Mulder, J,
2016-01-01
This paper investigates the classical type I and type II error probabilities of default Bayes factors for a Bayesian t test. Default Bayes factors quantify the relative evidence between the null hypothesis and the unrestricted alternative hypothesis without needing to specify prior distributions for
Bayesian Averaging is Well-Temperated
DEFF Research Database (Denmark)
Hansen, Lars Kai
2000-01-01
Bayesian predictions are stochastic just like predictions of any other inference scheme that generalize from a finite sample. While a simple variational argument shows that Bayes averaging is generalization optimal given that the prior matches the teacher parameter distribution the situation is l...
Robust bayesian inference of generalized Pareto distribution ...
African Journals Online (AJOL)
En utilisant une etude exhaustive de Monte Carlo, nous prouvons que, moyennant une fonction perte generalisee adequate, on peut construire un estimateur Bayesien robuste du modele. Key words: Bayesian estimation; Extreme value; Generalized Fisher information; Gener- alized Pareto distribution; Monte Carlo; ...
Evidence Estimation for Bayesian Partially Observed MRFs
Chen, Y.; Welling, M.
2013-01-01
Bayesian estimation in Markov random fields is very hard due to the intractability of the partition function. The introduction of hidden units makes the situation even worse due to the presence of potentially very many modes in the posterior distribution. For the first time we propose a
Inverse Problems in a Bayesian Setting
Matthies, Hermann G.; Zander, Elmar; Rosić, Bojana V.; Litvinenko, Alexander; Pajonk, Oliver
2016-01-01
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.
A Bayesian perspective on some replacement strategies
International Nuclear Information System (INIS)
Mazzuchi, Thomas A.; Soyer, Refik
1996-01-01
In this paper we present a Bayesian decision theoretic approach for determining optimal replacement strategies. This approach enables us to formally incorporate, express, and update our uncertainty when determining optimal replacement strategies. We develop relevant expressions for both the block replacement protocol with minimal repair and the age replacement protocol and illustrate the use of our approach with real data
Comparison between Fisherian and Bayesian approach to ...
African Journals Online (AJOL)
... of its simplicity and optimality properties is normally used for two group cases. However, Bayesian approach is found to be better than Fisher's approach because of its low misclassification error rate. Keywords: variance-covariance matrices, centroids, prior probability, mahalanobis distance, probability of misclassification ...
BAYESIAN ESTIMATION OF THERMONUCLEAR REACTION RATES
Energy Technology Data Exchange (ETDEWEB)
Iliadis, C.; Anderson, K. S. [Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255 (United States); Coc, A. [Centre de Sciences Nucléaires et de Sciences de la Matière (CSNSM), CNRS/IN2P3, Univ. Paris-Sud, Université Paris–Saclay, Bâtiment 104, F-91405 Orsay Campus (France); Timmes, F. X.; Starrfield, S., E-mail: iliadis@unc.edu [School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1504 (United States)
2016-11-01
The problem of estimating non-resonant astrophysical S -factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present astrophysical S -factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p, γ ){sup 3}He, {sup 3}He({sup 3}He,2p){sup 4}He, and {sup 3}He( α , γ ){sup 7}Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.
Non-Linear Approximation of Bayesian Update
Litvinenko, Alexander
2016-01-01
We develop a non-linear approximation of expensive Bayesian formula. This non-linear approximation is applied directly to Polynomial Chaos Coefficients. In this way, we avoid Monte Carlo sampling and sampling error. We can show that the famous Kalman Update formula is a particular case of this update.
Comprehension and computation in Bayesian problem solving
Directory of Open Access Journals (Sweden)
Eric D. Johnson
2015-07-01
Full Text Available Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian reasoning relative to normalized formats (e.g. probabilities, percentages, both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on transparent Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e. transparent problem structures at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct versus incorrect reasoners depart, and how individual difference might influence this time point.
A Bayesian Approach to Interactive Retrieval
Tague, Jean M.
1973-01-01
A probabilistic model for interactive retrieval is presented. Bayesian statistical decision theory principles are applied: use of prior and sample information about the relationship of document descriptions to query relevance; maximization of expected value of a utility function, to the problem of optimally restructuring search strategies in an…
Encoding dependence in Bayesian causal networks
Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...
Forecasting nuclear power supply with Bayesian autoregression
International Nuclear Information System (INIS)
Beck, R.; Solow, J.L.
1994-01-01
We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)
A Bayesian Nonparametric Approach to Factor Analysis
DEFF Research Database (Denmark)
Piatek, Rémi; Papaspiliopoulos, Omiros
2018-01-01
This paper introduces a new approach for the inference of non-Gaussian factor models based on Bayesian nonparametric methods. It relaxes the usual normality assumption on the latent factors, widely used in practice, which is too restrictive in many settings. Our approach, on the contrary, does no...
Hierarchical Bayesian Models of Subtask Learning
Anglim, Jeromy; Wynton, Sarah K. A.
2015-01-01
The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking…
Non-Linear Approximation of Bayesian Update
Litvinenko, Alexander
2016-06-23
We develop a non-linear approximation of expensive Bayesian formula. This non-linear approximation is applied directly to Polynomial Chaos Coefficients. In this way, we avoid Monte Carlo sampling and sampling error. We can show that the famous Kalman Update formula is a particular case of this update.
Bayesian approach and application to operation safety
International Nuclear Information System (INIS)
Procaccia, H.; Suhner, M.Ch.
2003-01-01
The management of industrial risks requires the development of statistical and probabilistic analyses which use all the available convenient information in order to compensate the insufficient experience feedback in a domain where accidents and incidents remain too scarce to perform a classical statistical frequency analysis. The Bayesian decision approach is well adapted to this problem because it integrates both the expertise and the experience feedback. The domain of knowledge is widen, the forecasting study becomes possible and the decisions-remedial actions are strengthen thanks to risk-cost-benefit optimization analyzes. This book presents the bases of the Bayesian approach and its concrete applications in various industrial domains. After a mathematical presentation of the industrial operation safety concepts and of the Bayesian approach principles, this book treats of some of the problems that can be solved thanks to this approach: softwares reliability, controls linked with the equipments warranty, dynamical updating of databases, expertise modeling and weighting, Bayesian optimization in the domains of maintenance, quality control, tests and design of new equipments. A synthesis of the mathematical formulae used in this approach is given in conclusion. (J.S.)
Jones, Matt; Love, Bradley C
2011-08-01
The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls
A Bayesian analysis of the nucleon QCD sum rules
International Nuclear Information System (INIS)
Ohtani, Keisuke; Gubler, Philipp; Oka, Makoto
2011-01-01
QCD sum rules of the nucleon channel are reanalyzed, using the maximum-entropy method (MEM). This new approach, based on the Bayesian probability theory, does not restrict the spectral function to the usual ''pole + continuum'' form, allowing a more flexible investigation of the nucleon spectral function. Making use of this flexibility, we are able to investigate the spectral functions of various interpolating fields, finding that the nucleon ground state mainly couples to an operator containing a scalar diquark. Moreover, we formulate the Gaussian sum rule for the nucleon channel and find that it is more suitable for the MEM analysis to extract the nucleon pole in the region of its experimental value, while the Borel sum rule does not contain enough information to clearly separate the nucleon pole from the continuum. (orig.)
GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model
International Nuclear Information System (INIS)
Takaishi, Tetsuya
2015-01-01
The realized stochastic volatility (RSV) model that utilizes the realized volatility as additional information has been proposed to infer volatility of financial time series. We consider the Bayesian inference of the RSV model by the Hybrid Monte Carlo (HMC) algorithm. The HMC algorithm can be parallelized and thus performed on the GPU for speedup. The GPU code is developed with CUDA Fortran. We compare the computational time in performing the HMC algorithm on GPU (GTX 760) and CPU (Intel i7-4770 3.4GHz) and find that the GPU can be up to 17 times faster than the CPU. We also code the program with OpenACC and find that appropriate coding can achieve the similar speedup with CUDA Fortran
Bayesian Correlation Analysis for Sequence Count Data.
Directory of Open Access Journals (Sweden)
Daniel Sánchez-Taltavull
Full Text Available Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low-especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities' signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset.
Zinck, John W R; Rajora, Om P
2016-03-02
Knowledge of the historical distribution and postglacial phylogeography and evolution of a species is important to better understand its current distribution and population structure and potential fate in the future, especially under climate change conditions, and conservation of its genetic resources. We have addressed this issue in a wide-ranging and heavily exploited keystone forest tree species of eastern North America, eastern white pine (Pinus strobus). We examined the range-wide population genetic structure, tested various hypothetical population history and evolutionary scenarios and inferred the location of glacial refugium and post-glacial recolonization routes. Our hypothesis was that eastern white pine survived in a single glacial refugium and expanded through multiple post-glacial recolonization routes. We studied the range-wide genetic diversity and population structure of 33 eastern white pine populations using 12 nuclear and 3 chloroplast microsatellite DNA markers. We used Approximate Bayesian Computation approach to test various evolutionary scenarios. We observed high levels of genetic diversity, and significant genetic differentiation (F ST = 0.104) and population structure among eastern white pine populations across its range. A south to north trend of declining genetic diversity existed, consistent with repeated founder effects during post-glaciation migration northwards. We observed broad consensus from nuclear and chloroplast genetic markers supporting the presence of two main post-glacial recolonization routes that originated from a single southern refugium in the mid-Atlantic plain. One route gave rise to populations at the western margin of the species' range in Minnesota and western Ontario. The second route gave rise to central-eastern populations, which branched into two subgroups: central and eastern. We observed minimal sharing of chloroplast haplotypes between recolonization routes but there was evidence of admixture between the
Quantum-Like Bayesian Networks for Modeling Decision Making
Directory of Open Access Journals (Sweden)
Catarina eMoreira
2016-01-01
Full Text Available In this work, we explore an alternative quantum structure to perform quantum probabilistic inferences to accommodate the paradoxical findings of the Sure Thing Principle. We propose a Quantum-Like Bayesian Network, which consists in replacing classical probabilities by quantum probability amplitudes. However, since this approach suffers from the problem of exponential growth of quantum parameters, we also propose a similarity heuristic that automatically fits quantum parameters through vector similarities. This makes the proposed model general and predictive in contrast to the current state of the art models, which cannot be generalized for more complex decision scenarios and that only provide an explanatory nature for the observed paradoxes. In the end, the model that we propose consists in a nonparametric method for estimating inference effects from a statistical point of view. It is a statistical model that is simpler than the previous quantum dynamic and quantum-like models proposed in the literature. We tested the proposed network with several empirical data from the literature, mainly from the Prisoner's Dilemma game and the Two Stage Gambling game. The results obtained show that the proposed quantum Bayesian Network is a general method that can accommodate violations of the laws of classical probability theory and make accurate predictions regarding human decision-making in these scenarios.
Prior Sensitivity Analysis in Default Bayesian Structural Equation Modeling.
van Erp, Sara; Mulder, Joris; Oberski, Daniel L
2017-11-27
Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models and solve some of the issues often encountered in classical maximum likelihood estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information. However, the prior can have a serious influence on the estimation of the model parameters, which affects the mean squared error, bias, coverage rates, and quantiles of the estimates. In this article, we investigate the performance of three different default priors: noninformative improper priors, vague proper priors, and empirical Bayes priors-with the latter being novel in the BSEM literature. Based on a simulation study, we find that these three default BSEM methods may perform very differently, especially with small samples. A careful prior sensitivity analysis is therefore needed when performing a default BSEM analysis. For this purpose, we provide a practical step-by-step guide for practitioners to conducting a prior sensitivity analysis in default BSEM. Our recommendations are illustrated using a well-known case study from the structural equation modeling literature, and all code for conducting the prior sensitivity analysis is available in the online supplemental materials. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting.
McGivney, Debra; Deshmane, Anagha; Jiang, Yun; Ma, Dan; Badve, Chaitra; Sloan, Andrew; Gulani, Vikas; Griswold, Mark
2018-07-01
To estimate multiple components within a single voxel in magnetic resonance fingerprinting when the number and types of tissues comprising the voxel are not known a priori. Multiple tissue components within a single voxel are potentially separable with magnetic resonance fingerprinting as a result of differences in signal evolutions of each component. The Bayesian framework for inverse problems provides a natural and flexible setting for solving this problem when the tissue composition per voxel is unknown. Assuming that only a few entries from the dictionary contribute to a mixed signal, sparsity-promoting priors can be placed upon the solution. An iterative algorithm is applied to compute the maximum a posteriori estimator of the posterior probability density to determine the magnetic resonance fingerprinting dictionary entries that contribute most significantly to mixed or pure voxels. Simulation results show that the algorithm is robust in finding the component tissues of mixed voxels. Preliminary in vivo data confirm this result, and show good agreement in voxels containing pure tissue. The Bayesian framework and algorithm shown provide accurate solutions for the partial-volume problem in magnetic resonance fingerprinting. The flexibility of the method will allow further study into different priors and hyperpriors that can be applied in the model. Magn Reson Med 80:159-170, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
Bayesian inference from count data using discrete uniform priors.
Directory of Open Access Journals (Sweden)
Federico Comoglio
Full Text Available We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. We report a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. Our derivation yields a computationally feasible formula that can prove useful in a variety of statistical problems involving absolute quantification under uncertainty. We implemented our algorithm in the R package dupiR and compared it with a previously proposed Bayesian method based on a Gamma prior. As a showcase, we demonstrate that our inference framework can be used to estimate bacterial survival curves from measurements characterized by extremely low or zero counts and rather high sampling fractions. All in all, we provide a versatile, general purpose algorithm to infer population sizes from count data, which can find application in a broad spectrum of biological and physical problems.
Approximate Bayesian computation for forward modeling in cosmology
International Nuclear Information System (INIS)
Akeret, Joël; Refregier, Alexandre; Amara, Adam; Seehars, Sebastian; Hasner, Caspar
2015-01-01
Bayesian inference is often used in cosmology and astrophysics to derive constraints on model parameters from observations. This approach relies on the ability to compute the likelihood of the data given a choice of model parameters. In many practical situations, the likelihood function may however be unavailable or intractable due to non-gaussian errors, non-linear measurements processes, or complex data formats such as catalogs and maps. In these cases, the simulation of mock data sets can often be made through forward modeling. We discuss how Approximate Bayesian Computation (ABC) can be used in these cases to derive an approximation to the posterior constraints using simulated data sets. This technique relies on the sampling of the parameter set, a distance metric to quantify the difference between the observation and the simulations and summary statistics to compress the information in the data. We first review the principles of ABC and discuss its implementation using a Population Monte-Carlo (PMC) algorithm and the Mahalanobis distance metric. We test the performance of the implementation using a Gaussian toy model. We then apply the ABC technique to the practical case of the calibration of image simulations for wide field cosmological surveys. We find that the ABC analysis is able to provide reliable parameter constraints for this problem and is therefore a promising technique for other applications in cosmology and astrophysics. Our implementation of the ABC PMC method is made available via a public code release
A Bayesian Approach for Sensor Optimisation in Impact Identification
Directory of Open Access Journals (Sweden)
Vincenzo Mallardo
2016-11-01
Full Text Available This paper presents a Bayesian approach for optimizing the position of sensors aimed at impact identification in composite structures under operational conditions. The uncertainty in the sensor data has been represented by statistical distributions of the recorded signals. An optimisation strategy based on the genetic algorithm is proposed to find the best sensor combination aimed at locating impacts on composite structures. A Bayesian-based objective function is adopted in the optimisation procedure as an indicator of the performance of meta-models developed for different sensor combinations to locate various impact events. To represent a real structure under operational load and to increase the reliability of the Structural Health Monitoring (SHM system, the probability of malfunctioning sensors is included in the optimisation. The reliability and the robustness of the procedure is tested with experimental and numerical examples. Finally, the proposed optimisation algorithm is applied to a composite stiffened panel for both the uniform and non-uniform probability of impact occurrence.
A Bayesian framework for cosmic string searches in CMB maps
Energy Technology Data Exchange (ETDEWEB)
Ciuca, Razvan; Hernández, Oscar F., E-mail: razvan.ciuca@mail.mcgill.ca, E-mail: oscarh@physics.mcgill.ca [Department of Physics, McGill University, 3600 rue University, Montréal, QC, H3A 2T8 (Canada)
2017-08-01
There exists various proposals to detect cosmic strings from Cosmic Microwave Background (CMB) or 21 cm temperature maps. Current proposals do not aim to find the location of strings on sky maps, all of these approaches can be thought of as a statistic on a sky map. We propose a Bayesian interpretation of cosmic string detection and within that framework, we derive a connection between estimates of cosmic string locations and cosmic string tension G μ. We use this Bayesian framework to develop a machine learning framework for detecting strings from sky maps and outline how to implement this framework with neural networks. The neural network we trained was able to detect and locate cosmic strings on noiseless CMB temperature map down to a string tension of G μ=5 ×10{sup −9} and when analyzing a CMB temperature map that does not contain strings, the neural network gives a 0.95 probability that G μ≤2.3×10{sup −9}.
Bayesian approach for peak detection in two-dimensional chromatography.
Vivó-Truyols, Gabriel
2012-03-20
A new method for peak detection in two-dimensional chromatography is presented. In a first step, the method starts with a conventional one-dimensional peak detection algorithm to detect modulated peaks. In a second step, a sophisticated algorithm is constructed to decide which of the individual one-dimensional peaks have been originated from the same compound and should then be arranged in a two-dimensional peak. The merging algorithm is based on Bayesian inference. The user sets prior information about certain parameters (e.g., second-dimension retention time variability, first-dimension band broadening, chromatographic noise). On the basis of these priors, the algorithm calculates the probability of myriads of peak arrangements (i.e., ways of merging one-dimensional peaks), finding which of them holds the highest value. Uncertainty in each parameter can be accounted by adapting conveniently its probability distribution function, which in turn may change the final decision of the most probable peak arrangement. It has been demonstrated that the Bayesian approach presented in this paper follows the chromatographers' intuition. The algorithm has been applied and tested with LC × LC and GC × GC data and takes around 1 min to process chromatograms with several thousands of peaks.
Molitor, John
2012-03-01
Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.
Bagley, Justin C; Sandel, Michael; Travis, Joseph; Lozano-Vilano, María de Lourdes; Johnson, Jerald B
2013-10-09
Climatic and sea-level fluctuations throughout the last Pleistocene glacial cycle (~130-0 ka) profoundly influenced present-day distributions and genetic diversity of Northern Hemisphere biotas by forcing range contractions in many species during the glacial advance and allowing expansion following glacial retreat ('expansion-contraction' model). Evidence for such range dynamics and refugia in the unglaciated Gulf-Atlantic Coastal Plain stems largely from terrestrial species, and aquatic species Pleistocene responses remain relatively uninvestigated. Heterandria formosa, a wide-ranging regional endemic, presents an ideal system to test the expansion-contraction model within this biota. By integrating ecological niche modeling and phylogeography, we infer the Pleistocene history of this livebearing fish (Poeciliidae) and test for several predicted distributional and genetic effects of the last glaciation. Paleoclimatic models predicted range contraction to a single southwest Florida peninsula refugium during the Last Glacial Maximum, followed by northward expansion. We inferred spatial-population subdivision into four groups that reflect genetic barriers outside this refuge. Several other features of the genetic data were consistent with predictions derived from an expansion-contraction model: limited intraspecific divergence (e.g. mean mtDNA p-distance = 0.66%); a pattern of mtDNA diversity (mean Hd = 0.934; mean π = 0.007) consistent with rapid, recent population expansion; a lack of mtDNA isolation-by-distance; and clinal variation in allozyme diversity with higher diversity at lower latitudes near the predicted refugium. Statistical tests of mismatch distributions and coalescent simulations of the gene tree lent greater support to a scenario of post-glacial expansion and diversification from a single refugium than to any other model examined (e.g. multiple-refugia scenarios). Congruent results from diverse data indicate H. formosa fits the classic Pleistocene
2008-01-01
Background Tectonic, volcanic and climatic events that produce changes in hydrographic systems are the main causes of diversification and speciation of freshwater fishes. Elucidate the evolutionary history of freshwater fishes permits to infer theories on the biotic and geological evolution of a region, which can further be applied to understand processes of population divergence, speciation and for conservation purposes. The freshwater ecosystems in Central Mexico are characterized by their genesis dynamism, destruction, and compartmentalization induced by intense geologic activity and climatic changes since the early Miocene. The endangered goodeid Zoogoneticus quitzeoensis is widely distributed across Central México, thus making it a good model for phylogeographic analyses in this area. Results We addressed the phylogeography, evolutionary history and genetic structure of populations of Z. quitzeoensis through a sequential approach, based on both microsatellite and mitochondrial cytochrome b sequences. Most haplotypes were private to particular locations. All the populations analysed showed a remarkable number of haplotypes. The level of gene diversity within populations was H¯d = 0.987 (0.714 – 1.00). However, in general the nucleotide diversity was low, π = 0.0173 (0.0015 – 0.0049). Significant genetic structure was found among populations at the mitochondrial and nuclear level (ΦST = 0.836 and FST = 0.262, respectively). We distinguished two well-defined mitochondrial lineages that were separated ca. 3.3 million years ago (Mya). The time since expansion was ca. 1.5 × 106 years ago for Lineage I and ca. 860,000 years ago for Lineage II. Also, genetic patterns of differentiation, between and within lineages, are described at different historical timescales. Conclusion Our mtDNA data indicates that the evolution of the different genetic groups is more related to ancient geological and climatic events (Middle Pliocene, ca. 3.3 Mya) than to the current
Directory of Open Access Journals (Sweden)
Huu eHoang
2015-05-01
Full Text Available The inverse problem for estimating model parameters from brain spike data is an ill-posed problem because of a huge mismatch in the system complexity between the model and the brain as well as its non-stationary dynamics, and needs a stochastic approach that finds the most likely solution among many possible solutions. In the present study, we developed a segmental Bayesian method to estimate the two parameters of interest, the gap-junctional (gc and inhibitory conductance (gi from inferior olive spike data. Feature vectors were estimated for the spike data in a segment-wise fashion to compensate for the non-stationary firing dynamics. Hierarchical Bayesian estimation was conducted to estimate the gc and gi for every spike segment using a forward model constructed in the principal component analysis (PCA space of the feature vectors, and to merge the segmental estimates into single estimates for every neuron. The segmental Bayesian estimation gave smaller fitting errors than the conventional Bayesian inference, which finds the estimates once across the entire spike data, or the minimum error method, which directly finds the closest match in the PCA space. The segmental Bayesian inference has the potential to overcome the problem of non-stationary dynamics and resolve the ill-posedness of the inverse problem because of the mismatch between the model and the brain under the constraints based, and it is a useful tool to evaluate parameters of interest for neuroscience from experimental spike train data.
Can natural selection encode Bayesian priors?
Ramírez, Juan Camilo; Marshall, James A R
2017-08-07
The evolutionary success of many organisms depends on their ability to make decisions based on estimates of the state of their environment (e.g., predation risk) from uncertain information. These decision problems have optimal solutions and individuals in nature are expected to evolve the behavioural mechanisms to make decisions as if using the optimal solutions. Bayesian inference is the optimal method to produce estimates from uncertain data, thus natural selection is expected to favour individuals with the behavioural mechanisms to make decisions as if they were computing Bayesian estimates in typically-experienced environments, although this does not necessarily imply that favoured decision-makers do perform Bayesian computations exactly. Each individual should evolve to behave as if updating a prior estimate of the unknown environment variable to a posterior estimate as it collects evidence. The prior estimate represents the decision-maker's default belief regarding the environment variable, i.e., the individual's default 'worldview' of the environment. This default belief has been hypothesised to be shaped by natural selection and represent the environment experienced by the individual's ancestors. We present an evolutionary model to explore how accurately Bayesian prior estimates can be encoded genetically and shaped by natural selection when decision-makers learn from uncertain information. The model simulates the evolution of a population of individuals that are required to estimate the probability of an event. Every individual has a prior estimate of this probability and collects noisy cues from the environment in order to update its prior belief to a Bayesian posterior estimate with the evidence gained. The prior is inherited and passed on to offspring. Fitness increases with the accuracy of the posterior estimates produced. Simulations show that prior estimates become accurate over evolutionary time. In addition to these 'Bayesian' individuals, we also
Hoyal Cuthill, Jennifer F; Charleston, Michael
2015-12-01
Examples of long-term coevolution are rare among free-living organisms. Müllerian mimicry in Heliconius butterflies had been suggested as a key example of coevolution by early genetic studies. However, research over the last two decades has been dominated by the idea that the best-studied comimics, H. erato and H. melpomene, did not coevolve at all. Recently sequenced genes associated with wing color pattern phenotype offer a new opportunity to resolve this controversy. Here, we test the hypothesis of coevolution between H. erato and H. melpomene using Bayesian multilocus analysis of five color pattern genes and five neutral genetic markers. We first explore the extent of phylogenetic agreement versus conflict between the different genes. Coevolution is then tested against three aspects of the mimicry diversifications: phylogenetic branching patterns, divergence times, and, for the first time, phylogeographic histories. We show that all three lines of evidence are compatible with strict coevolution of the diverse mimicry wing patterns, contrary to some recent suggestions. Instead, these findings tally with a coevolutionary diversification driven primarily by the ecological force of Müllerian mimicry. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.
Bayesianism and inference to the best explanation
Directory of Open Access Journals (Sweden)
Valeriano IRANZO
2008-01-01
Full Text Available Bayesianism and Inference to the best explanation (IBE are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretations of prior probabilities: “IBE-Bayesianism” (IBE-Bay and “frequentist-Bayesianism” (Freq-Bay. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: (i endorses a role for explanatory value in the assessment of scientific hypotheses; (ii avoids a purely subjectivist reading of prior probabilities; and (iii fits better than IBE-Bayesianism with two basic facts about science, i.e., the prominent role played by empirical testing and the existence of many scientific theories in the past that failed to fulfil their promises and were subsequently abandoned.
Modelling dependable systems using hybrid Bayesian networks
International Nuclear Information System (INIS)
Neil, Martin; Tailor, Manesh; Marquez, David; Fenton, Norman; Hearty, Peter
2008-01-01
A hybrid Bayesian network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment, the models are invariably hybrid and the need for efficient and accurate computation is paramount. We apply a new iterative algorithm that efficiently combines dynamic discretisation with robust propagation algorithms on junction tree structures to perform inference in hybrid BNs. We illustrate its use in the field of dependability with two example of reliability estimation. Firstly we estimate the reliability of a simple single system and next we implement a hierarchical Bayesian model. In the hierarchical model we compute the reliability of two unknown subsystems from data collected on historically similar subsystems and then input the result into a reliability block model to compute system level reliability. We conclude that dynamic discretisation can be used as an alternative to analytical or Monte Carlo methods with high precision and can be applied to a wide range of dependability problems
Bayesian Peak Picking for NMR Spectra
Cheng, Yichen
2014-02-01
Protein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein–DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR) has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.
Bayesian component separation: The Planck experience
Wehus, Ingunn Kathrine; Eriksen, Hans Kristian
2018-05-01
Bayesian component separation techniques have played a central role in the data reduction process of Planck. The most important strength of this approach is its global nature, in which a parametric and physical model is fitted to the data. Such physical modeling allows the user to constrain very general data models, and jointly probe cosmological, astrophysical and instrumental parameters. This approach also supports statistically robust goodness-of-fit tests in terms of data-minus-model residual maps, which are essential for identifying residual systematic effects in the data. The main challenges are high code complexity and computational cost. Whether or not these costs are justified for a given experiment depends on its final uncertainty budget. We therefore predict that the importance of Bayesian component separation techniques is likely to increase with time for intensity mapping experiments, similar to what has happened in the CMB field, as observational techniques mature, and their overall sensitivity improves.
Bayesian Modelling of Functional Whole Brain Connectivity
DEFF Research Database (Denmark)
Røge, Rasmus
the prevalent strategy of standardizing of fMRI time series and model data using directional statistics or we model the variability in the signal across the brain and across multiple subjects. In either case, we use Bayesian nonparametric modeling to automatically learn from the fMRI data the number......This thesis deals with parcellation of whole-brain functional magnetic resonance imaging (fMRI) using Bayesian inference with mixture models tailored to the fMRI data. In the three included papers and manuscripts, we analyze two different approaches to modeling fMRI signal; either we accept...... of funcional units, i.e. parcels. We benchmark the proposed mixture models against state of the art methods of brain parcellation, both probabilistic and non-probabilistic. The time series of each voxel are most often standardized using z-scoring which projects the time series data onto a hypersphere...
Software Health Management with Bayesian Networks
Mengshoel, Ole; Schumann, JOhann
2011-01-01
Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.
Narrowband interference parameterization for sparse Bayesian recovery
Ali, Anum
2015-09-11
This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. © 2015 IEEE.
Machine learning a Bayesian and optimization perspective
Theodoridis, Sergios
2015-01-01
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as shor...
Bayesian calibration : past achievements and future challenges
International Nuclear Information System (INIS)
Christen, J.A.
2001-01-01
Due to variations of the radiocarbon content in the biosphere over time, radiocarbon determinations need to be calibrated to obtain calendar years. Over the past decade a series of researchers have investigated the possibility of using Bayesian statistics to calibrate radiocarbon determinations, the main feature being the inclusion of contextual information into the calibration process. This allows for a coherent calibration of groups of determinations arising from related contexts (stratigraphical layers, peat cores, cultural events, ect.). Moreover, the 'related contexts' are also dated, and not only the material radiocarbon dated itself. We review Bayesian Calibration and state some of its current challenges like: software development, prior specification, robustness, etc. (author). 14 refs., 4 figs
Disentangling Complexity in Bayesian Automatic Adaptive Quadrature
Adam, Gheorghe; Adam, Sanda
2018-02-01
The paper describes a Bayesian automatic adaptive quadrature (BAAQ) solution for numerical integration which is simultaneously robust, reliable, and efficient. Detailed discussion is provided of three main factors which contribute to the enhancement of these features: (1) refinement of the m-panel automatic adaptive scheme through the use of integration-domain-length-scale-adapted quadrature sums; (2) fast early problem complexity assessment - enables the non-transitive choice among three execution paths: (i) immediate termination (exceptional cases); (ii) pessimistic - involves time and resource consuming Bayesian inference resulting in radical reformulation of the problem to be solved; (iii) optimistic - asks exclusively for subrange subdivision by bisection; (3) use of the weaker accuracy target from the two possible ones (the input accuracy specifications and the intrinsic integrand properties respectively) - results in maximum possible solution accuracy under minimum possible computing time.
Bayesian network modelling of upper gastrointestinal bleeding
Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri
2013-09-01
Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.
Bayesian Prior Probability Distributions for Internal Dosimetry
Energy Technology Data Exchange (ETDEWEB)
Miller, G.; Inkret, W.C.; Little, T.T.; Martz, H.F.; Schillaci, M.E
2001-07-01
The problem of choosing a prior distribution for the Bayesian interpretation of measurements (specifically internal dosimetry measurements) is considered using a theoretical analysis and by examining historical tritium and plutonium urine bioassay data from Los Alamos. Two models for the prior probability distribution are proposed: (1) the log-normal distribution, when there is some additional information to determine the scale of the true result, and (2) the 'alpha' distribution (a simplified variant of the gamma distribution) when there is not. These models have been incorporated into version 3 of the Bayesian internal dosimetric code in use at Los Alamos (downloadable from our web site). Plutonium internal dosimetry at Los Alamos is now being done using prior probability distribution parameters determined self-consistently from population averages of Los Alamos data. (author)
Probabilistic forecasting and Bayesian data assimilation
Reich, Sebastian
2015-01-01
In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in ap...
Bayesian networks and boundedly rational expectations
Ran Spiegler
2014-01-01
I present a framework for analyzing decision makers with an imperfect understanding of their environment's correlation structure. The framework borrows the tool of "Bayesian networks", which is ubiquitous in statistics and artificial intelligence. In the model, a decision maker faces an objective multivariate probability distribution (his own action is one of the random variables). He is characterized by a directed acyclic graph over the set of random variables. His subjective belief filters ...
Approximation of Bayesian Inverse Problems for PDEs
Cotter, S. L.; Dashti, M.; Stuart, A. M.
2010-01-01
Inverse problems are often ill posed, with solutions that depend sensitively on data.n any numerical approach to the solution of such problems, regularization of some form is needed to counteract the resulting instability. This paper is based on an approach to regularization, employing a Bayesian formulation of the problem, which leads to a notion of well posedness for inverse problems, at the level of probability measures. The stability which results from this well posedness may be used as t...
Multilevel Monte Carlo in Approximate Bayesian Computation
Jasra, Ajay
2017-02-13
In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation. Several numerical examples are given.
Centralized Bayesian reliability modelling with sensor networks
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
2013-01-01
Roč. 19, č. 5 (2013), s. 471-482 ISSN 1387-3954 R&D Projects: GA MŠk 7D12004 Grant - others:GA MŠk(CZ) SVV-265315 Keywords : Bayesian modelling * Sensor network * Reliability Subject RIV: BD - Theory of Information Impact factor: 0.984, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/dedecius-0392551.pdf
Essays on portfolio choice with Bayesian methods
Kebabci, Deniz
2007-01-01
How investors should allocate assets to their portfolios in the presence of predictable components in asset returns is a question of great importance in finance. While early studies took the return generating process as given, recent studies have addressed issues such as parameter estimation and model uncertainty. My dissertation develops Bayesian methods for portfolio choice - and industry allocation in particular - under parameter and model uncertainty. The first chapter of my dissertation,...
Bayesian estimation of Weibull distribution parameters
International Nuclear Information System (INIS)
Bacha, M.; Celeux, G.; Idee, E.; Lannoy, A.; Vasseur, D.
1994-11-01
In this paper, we expose SEM (Stochastic Expectation Maximization) and WLB-SIR (Weighted Likelihood Bootstrap - Sampling Importance Re-sampling) methods which are used to estimate Weibull distribution parameters when data are very censored. The second method is based on Bayesian inference and allow to take into account available prior informations on parameters. An application of this method, with real data provided by nuclear power plants operation feedback analysis has been realized. (authors). 8 refs., 2 figs., 2 tabs
A theory of Bayesian decision making
Karni, Edi
2009-01-01
This paper presents a complete, choice-based, axiomatic Bayesian decision theory. It introduces a new choice set consisting of information-contingent plans for choosing actions and bets and subjective expected utility model with effect-dependent utility functions and action-dependent subjective probabilities which, in conjunction with the updating of the probabilities using Bayes’ rule, gives rise to a unique prior and a set of action-dependent posterior probabilities representing the decisio...
Constrained bayesian inference of project performance models
Sunmola, Funlade
2013-01-01
Project performance models play an important role in the management of project success. When used for monitoring projects, they can offer predictive ability such as indications of possible delivery problems. Approaches for monitoring project performance relies on available project information including restrictions imposed on the project, particularly the constraints of cost, quality, scope and time. We study in this paper a Bayesian inference methodology for project performance modelling in ...
Bayesian Estimation and Inference using Stochastic Hardware
Directory of Open Access Journals (Sweden)
Chetan Singh Thakur
2016-03-01
Full Text Available In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker, demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND, we show how inference can be performed in a Directed Acyclic Graph (DAG using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.
Bayesian Estimation and Inference Using Stochastic Electronics.
Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André
2016-01-01
In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.
Virtual Vector Machine for Bayesian Online Classification
Minka, Thomas P.; Xiang, Rongjing; Yuan; Qi
2012-01-01
In a typical online learning scenario, a learner is required to process a large data stream using a small memory buffer. Such a requirement is usually in conflict with a learner's primary pursuit of prediction accuracy. To address this dilemma, we introduce a novel Bayesian online classi cation algorithm, called the Virtual Vector Machine. The virtual vector machine allows you to smoothly trade-off prediction accuracy with memory size. The virtual vector machine summarizes the information con...
Characteristic imsets for learning Bayesian network structure
Czech Academy of Sciences Publication Activity Database
Hemmecke, R.; Lindner, S.; Studený, Milan
2012-01-01
Roč. 53, č. 9 (2012), s. 1336-1349 ISSN 0888-613X R&D Projects: GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * essential graph * standard imset * characteristic imset * LP relaxation of a polytope Subject RIV: BA - General Mathematics Impact factor: 1.729, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/studeny-0382596.pdf
Bayesian analysis of Markov point processes
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
2006-01-01
Recently Møller, Pettitt, Berthelsen and Reeves introduced a new MCMC methodology for drawing samples from a posterior distribution when the likelihood function is only specified up to a normalising constant. We illustrate the method in the setting of Bayesian inference for Markov point processes...... a partially ordered Markov point process as the auxiliary variable. As the method requires simulation from the "unknown" likelihood, perfect simulation algorithms for spatial point processes become useful....
Decisions under uncertainty using Bayesian analysis
Directory of Open Access Journals (Sweden)
Stelian STANCU
2006-01-01
Full Text Available The present paper makes a short presentation of the Bayesian decions method, where extrainformation brings a great support to decision making process, but also attract new costs. In this situation, getting new information, generally experimentaly based, contributes to diminushing the uncertainty degree that influences decision making process. As a conclusion, in a large number of decision problems, there is the possibility that the decision makers will renew some decisions already taken because of the facilities offered by obtainig extrainformation.
Network structure exploration via Bayesian nonparametric models
International Nuclear Information System (INIS)
Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z
2015-01-01
Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)
Bayesian analyses of seasonal runoff forecasts
Krzysztofowicz, R.; Reese, S.
1991-12-01
Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.
Bayesian Recurrent Neural Network for Language Modeling.
Chien, Jen-Tzung; Ku, Yuan-Chu
2016-02-01
A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.
Bayesian Analysis of Individual Level Personality Dynamics
Directory of Open Access Journals (Sweden)
Edward Cripps
2016-07-01
Full Text Available A Bayesian technique with analyses of within-person processes at the level of the individual is presented. The approach is used to examine if the patterns of within-person responses on a 12 trial simulation task are consistent with the predictions of ITA theory (Dweck, 1999. ITA theory states that the performance of an individual with an entity theory of ability is more likely to spiral down following a failure experience than the performance of an individual with an incremental theory of ability. This is because entity theorists interpret failure experiences as evidence of a lack of ability, which they believe is largely innate and therefore relatively ﬁxed; whilst incremental theorists believe in the malleability of abilities and interpret failure experiences as evidence of more controllable factors such as poor strategy or lack of effort. The results of our analyses support ITA theory at both the within- and between-person levels of analyses and demonstrate the beneﬁts of Bayesian techniques for the analysis of within-person processes. These include more formal speciﬁcation of the theory and the ability to draw inferences about each individual, which allows for more nuanced interpretations of individuals within a personality category, such as differences in the individual probabilities of spiralling. While Bayesian techniques have many potential advantages for the analyses of within-person processes at the individual level, ease of use is not one of them for psychologists trained in traditional frequentist statistical techniques.
Particle identification in ALICE: a Bayesian approach
Adam, Jaroslav; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahmad, Shakeel; Ahn, Sang Un; Aiola, Salvatore; Akindinov, Alexander; Alam, Sk Noor; Silva De Albuquerque, Danilo; Aleksandrov, Dmitry; Alessandro, Bruno; Alexandre, Didier; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Millan Almaraz, Jesus Roberto; Alme, Johan; Alt, Torsten; Altinpinar, Sedat; Altsybeev, Igor; Alves Garcia Prado, Caio; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Arnaldi, Roberta; Arnold, Oliver Werner; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Balasubramanian, Supraja; Baldisseri, Alberto; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Barth, Klaus; Bartke, Jerzy Gustaw; Bartsch, Esther; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batista Camejo, Arianna; Batyunya, Boris; Batzing, Paul Christoph; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Belmont Iii, Ronald John; Belmont Moreno, Ernesto; Belyaev, Vladimir; Benacek, Pavel; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhat, Inayat Rasool; Bhati, Ashok Kumar; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biro, Gabor; Biswas, Rathijit; Biswas, Saikat; Bjelogrlic, Sandro; Blair, Justin Thomas; Blau, Dmitry; Blume, Christoph; Bock, Friederike; Bogdanov, Alexey; Boggild, Hans; Boldizsar, Laszlo; Bombara, Marek; Book, Julian Heinz; Borel, Herve; Borissov, Alexander; Borri, Marcello; Bossu, Francesco; Botta, Elena; Bourjau, Christian; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Broker, Theo Alexander; Browning, Tyler Allen; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Bashir Butt, Jamila; Buxton, Jesse Thomas; Cabala, Jan; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calero Diaz, Liliet; Caliva, Alberto; Calvo Villar, Ernesto; Camerini, Paolo; Carena, Francesco; Carena, Wisla; Carnesecchi, Francesca; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Cerkala, Jakub; Chang, Beomsu; Chapeland, Sylvain; Chartier, Marielle; Charvet, Jean-Luc Fernand; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chauvin, Alex; Chelnokov, Volodymyr; Cherney, Michael Gerard; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Cho, Soyeon; Chochula, Peter; Choi, Kyungeon; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Connors, Megan Elizabeth; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortes Maldonado, Ismael; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Crochet, Philippe; Cruz Albino, Rigoberto; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Danisch, Meike Charlotte; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Conti, Camila; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Deisting, Alexander; Deloff, Andrzej; Denes, Ervin Sandor; Deplano, Caterina; Dhankher, Preeti; Di Bari, Domenico; Di Mauro, Antonio; Di Nezza, Pasquale; Diaz Corchero, Miguel Angel; Dietel, Thomas; Dillenseger, Pascal; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Drozhzhova, Tatiana; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Endress, Eric; Engel, Heiko; Epple, Eliane; Erazmus, Barbara Ewa; Erdemir, Irem; Erhardt, Filip; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Eum, Jongsik; Evans, David; Evdokimov, Sergey; Eyyubova, Gyulnara; Fabbietti, Laura; Fabris, Daniela; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Feliciello, Alessandro; Feofilov, Grigorii; Ferencei, Jozef; Fernandez Tellez, Arturo; Gonzalez Ferreiro, Elena; Ferretti, Alessandro; Festanti, Andrea; Feuillard, Victor Jose Gaston; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiore, Enrichetta Maria; Fleck, Martin Gabriel; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fronze, Gabriele Gaetano; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Gao, Chaosong; Garabatos Cuadrado, Jose; Garcia-Solis, Edmundo Javier; Gargiulo, Corrado; Gasik, Piotr Jan; Gauger, Erin Frances; Germain, Marie; Gheata, Andrei George; Gheata, Mihaela; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez Coral, Diego Mauricio; Gomez Ramirez, Andres; Sanchez Gonzalez, Andres; Gonzalez, Victor; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Grachov, Oleg Anatolievich; Graczykowski, Lukasz Kamil; Graham, Katie Leanne; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Grynyov, Borys; Grion, Nevio; Gronefeld, Julius Maximilian; Grosse-Oetringhaus, Jan Fiete; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerzoni, Barbara; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Haake, Rudiger; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Hamon, Julien Charles; Harris, John William; Harton, Austin Vincent; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Hellbar, Ernst; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Hess, Benjamin Andreas; Hetland, Kristin Fanebust; Hillemanns, Hartmut; Hippolyte, Boris; Horak, David; Hosokawa, Ritsuya; Hristov, Peter Zahariev; Humanic, Thomas; Hussain, Nur; Hussain, Tahir; Hutter, Dirk; Hwang, Dae Sung; Ilkaev, Radiy; Inaba, Motoi; Incani, Elisa; Ippolitov, Mikhail; Irfan, Muhammad; Ivanov, Marian; Ivanov, Vladimir; Izucheev, Vladimir; Jacazio, Nicolo; Jacobs, Peter Martin; Jadhav, Manoj Bhanudas; Jadlovska, Slavka; Jadlovsky, Jan; Jahnke, Cristiane; Jakubowska, Monika Joanna; Jang, Haeng Jin; Janik, Malgorzata Anna; Pahula Hewage, Sandun; Jena, Chitrasen; Jena, Satyajit; Jimenez Bustamante, Raul Tonatiuh; Jones, Peter Graham; Jusko, Anton; Kalinak, Peter; Kalweit, Alexander Philipp; Kamin, Jason Adrian; Kang, Ju Hwan; Kaplin, Vladimir; Kar, Somnath; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karayan, Lilit; Karpechev, Evgeny; Kebschull, Udo Wolfgang; Keidel, Ralf; Keijdener, Darius Laurens; Keil, Markus; Khan, Mohammed Mohisin; Khan, Palash; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Do Won; Kim, Dong Jo; Kim, Daehyeok; Kim, Hyeonjoong; Kim, Jinsook; Kim, Minwoo; Kim, Se Yong; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Kiss, Gabor; Klay, Jennifer Lynn; Klein, Carsten; Klein, Jochen; Klein-Boesing, Christian; Klewin, Sebastian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kobdaj, Chinorat; Kofarago, Monika; Kollegger, Thorsten; Kolozhvari, Anatoly; Kondratev, Valerii; Kondratyeva, Natalia; Kondratyuk, Evgeny; Konevskikh, Artem; Kopcik, Michal; Kostarakis, Panagiotis; Kour, Mandeep; Kouzinopoulos, Charalampos; Kovalenko, Oleksandr; Kovalenko, Vladimir; Kowalski, Marek; Koyithatta Meethaleveedu, Greeshma; Kralik, Ivan; Kravcakova, Adela; Krivda, Marian; Krizek, Filip; Kryshen, Evgeny; Krzewicki, Mikolaj; Kubera, Andrew Michael; Kucera, Vit; Kuhn, Christian Claude; Kuijer, Paulus Gerardus; Kumar, Ajay; Kumar, Jitendra; Kumar, Lokesh; Kumar, Shyam; Kurashvili, Podist; Kurepin, Alexander; Kurepin, Alexey; Kuryakin, Alexey; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Ladron De Guevara, Pedro; Lagana Fernandes, Caio; Lakomov, Igor; Langoy, Rune; Lara Martinez, Camilo Ernesto; Lardeux, Antoine Xavier; Lattuca, Alessandra; Laudi, Elisa; Lea, Ramona; Leardini, Lucia; Lee, Graham Richard; Lee, Seongjoo; Lehas, Fatiha; Lemmon, Roy Crawford; Lenti, Vito; Leogrande, Emilia; Leon Monzon, Ildefonso; Leon Vargas, Hermes; Leoncino, Marco; Levai, Peter; Li, Shuang; Li, Xiaomei; Lien, Jorgen Andre; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Ljunggren, Hans Martin; Lodato, Davide Francesco; Lonne, Per-Ivar; Loginov, Vitaly; Loizides, Constantinos; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lowe, Andrew John; Luettig, Philipp Johannes; Lunardon, Marcello; Luparello, Grazia; Lutz, Tyler Harrison; Maevskaya, Alla; Mager, Magnus; Mahajan, Sanjay; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Liudmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martin Blanco, Javier; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-Garcia, Gines; Martinez Pedreira, Miguel; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Mastroserio, Annalisa; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel Anthony; Mazzoni, Alessandra Maria; Mcdonald, Daniel; Meddi, Franco; Melikyan, Yuri; Menchaca-Rocha, Arturo Alejandro; Meninno, Elisa; Mercado-Perez, Jorge; Meres, Michal; Miake, Yasuo; Mieskolainen, Matti Mikael; Mikhaylov, Konstantin; Milano, Leonardo; Milosevic, Jovan; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Montes Prado, Esther; Moreira De Godoy, Denise Aparecida; Perez Moreno, Luis Alberto; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Munzer, Robert Helmut; Murakami, Hikari; Murray, Sean; Musa, Luciano; Musinsky, Jan; Naik, Bharati; Nair, Rahul; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Naru, Muhammad Umair; Ferreira Natal Da Luz, Pedro Hugo; Nattrass, Christine; Rosado Navarro, Sebastian; Nayak, Kishora; Nayak, Ranjit; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nellen, Lukas; Ng, Fabian; Nicassio, Maria; Niculescu, Mihai; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Cabanillas Noris, Juan Carlos; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oeschler, Helmut Oskar; Oh, Saehanseul; Oh, Sun Kun; Ohlson, Alice Elisabeth; Okatan, Ali; Okubo, Tsubasa; Olah, Laszlo; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Orava, Risto; Oravec, Matej; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozdemir, Mahmut; Pachmayer, Yvonne Chiara; Pagano, Davide; Pagano, Paola; Paic, Guy; Pal, Susanta Kumar; Pan, Jinjin; Pandey, Ashutosh Kumar; Papikyan, Vardanush; Pappalardo, Giuseppe; Pareek, Pooja; Park, Woojin; Parmar, Sonia; Passfeld, Annika; Paticchio, Vincenzo; Patra, Rajendra Nath; Paul, Biswarup; Pei, Hua; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Peresunko, Dmitry Yurevich; Perez Lara, Carlos Eugenio; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrov, Viacheslav; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Ozelin De Lima Pimentel, Lais; Pinazza, Ombretta; Pinsky, Lawrence; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Porteboeuf, Sarah Julie; Porter, R Jefferson; Pospisil, Jan; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Puddu, Giovanna; Pujahari, Prabhat Ranjan; Punin, Valery; Putschke, Jorn Henning; Qvigstad, Henrik; Rachevski, Alexandre; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Rami, Fouad; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Redlich, Krzysztof; Reed, Rosi Jan; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Reygers, Klaus Johannes; Riabov, Viktor; Ricci, Renato Angelo; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-Lucian; Rocco, Elena; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roeed, Ketil; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Ronchetti, Federico; Ronflette, Lucile; Rosnet, Philippe; Rossi, Andrea; Roukoutakis, Filimon; Roy, Ankhi; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Saarinen, Sampo; Sadhu, Samrangy; Sadovskiy, Sergey; Safarik, Karel; Sahlmuller, Baldo; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Salzwedel, Jai Samuel Nielsen; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Sarkar, Debojit; Sarkar, Nachiketa; Sarma, Pranjal; Scapparone, Eugenio; Scarlassara, Fernando; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schuchmann, Simone; Schukraft, Jurgen; Schulc, Martin; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Rebecca Michelle; Sefcik, Michal; Seger, Janet Elizabeth; Sekiguchi, Yuko; Sekihata, Daiki; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Senyukov, Serhiy; Serradilla Rodriguez, Eulogio; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shadura, Oksana; Shahoyan, Ruben; Shahzad, Muhammed Ikram; Shangaraev, Artem; Sharma, Ankita; Sharma, Mona; Sharma, Monika; Sharma, Natasha; Sheikh, Ashik Ikbal; Shigaki, Kenta; Shou, Qiye; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine Micaela; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Song, Jihye; Song, Myunggeun; Song, Zixuan; Soramel, Francesca; Sorensen, Soren Pontoppidan; Derradi De Souza, Rafael; Sozzi, Federica; Spacek, Michal; Spiriti, Eleuterio; Sputowska, Iwona Anna; Spyropoulou-Stassinaki, Martha; Stachel, Johanna; Stan, Ionel; Stankus, Paul; Stenlund, Evert Anders; Steyn, Gideon Francois; Stiller, Johannes Hendrik; Stocco, Diego; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Suljic, Miljenko; Sultanov, Rishat; Sumbera, Michal; Sumowidagdo, Suharyo; Szabo, Alexander; Szanto De Toledo, Alejandro; Szarka, Imrich; Szczepankiewicz, Adam; Szymanski, Maciej Pawel; Tabassam, Uzma; Takahashi, Jun; Tambave, Ganesh Jagannath; Tanaka, Naoto; Tarhini, Mohamad; Tariq, Mohammad; Tarzila, Madalina-Gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terasaki, Kohei; Terrevoli, Cristina; Teyssier, Boris; Thaeder, Jochen Mathias; Thakur, Dhananjaya; Thomas, Deepa; Tieulent, Raphael Noel; Timmins, Anthony Robert; Toia, Alberica; Trogolo, Stefano; Trombetta, Giuseppe; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; Van Der Maarel, Jasper; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vanat, Tomas; Vande Vyvre, Pierre; Varga, Dezso; Diozcora Vargas Trevino, Aurora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vechernin, Vladimir; Veen, Annelies Marianne; Veldhoen, Misha; Velure, Arild; Vercellin, Ermanno; Vergara Limon, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Villatoro Tello, Abraham; Vinogradov, Alexander; Vinogradov, Leonid; Vinogradov, Yury; Virgili, Tiziano; Vislavicius, Vytautas; Viyogi, Yogendra; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Vranic, Danilo; Vrlakova, Janka; Vulpescu, Bogdan; Wagner, Boris; Wagner, Jan; Wang, Hongkai; Wang, Mengliang; Watanabe, Daisuke; Watanabe, Yosuke; Weber, Michael; Weber, Steffen Georg; Weiser, Dennis Franz; Wessels, Johannes Peter; Westerhoff, Uwe; Whitehead, Andile Mothegi; Wiechula, Jens; Wikne, Jon; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Williams, Crispin; Windelband, Bernd Stefan; Winn, Michael Andreas; Yang, Hongyan; Yang, Ping; Yano, Satoshi; Yasin, Zafar; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-Kwon; Yoon, Jin Hee; Yurchenko, Volodymyr; Yushmanov, Igor; Zaborowska, Anna; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correia Zanoli, Henrique Jose; Zaporozhets, Sergey; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhang, Yonghong; Chunhui, Zhang; Zhang, Zuman; Zhao, Chengxin; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zichichi, Antonino; Zimmermann, Alice; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zyzak, Maksym
2016-05-25
We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian PID approach for charged pions, kaons and protons in the central barrel of ALICE is studied. PID is performed via measurements of specific energy loss (dE/dx) and time-of-flight. PID efficiencies and misidentification probabilities are extracted and compared with Monte Carlo simulations using high purity samples of identified particles in the decay channels ${\\rm K}_{\\rm S}^{\\rm 0}\\rightarrow \\pi^+\\pi^-$, $\\phi\\rightarrow {\\rm K}^-{\\rm K}^+$ and $\\Lambda\\rightarrow{\\rm p}\\pi^-$ in p–Pb collisions at $\\sqrt{s_{\\rm NN}}= 5.02$TeV. In order to thoroughly assess the validity of the Bayesian approach, this methodology was used to obtain corrected $p_{\\rm T}$ spectra of pions, kaons, protons, and D$^0$ mesons in pp coll...
Discriminative Bayesian Dictionary Learning for Classification.
Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal
2016-12-01
We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.
Bayesian Inference of a Multivariate Regression Model
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Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Bayesian posterior distributions without Markov chains.
Cole, Stephen R; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B
2012-03-01
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976-1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984-1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.
Bayesian methodology for reliability model acceptance
International Nuclear Information System (INIS)
Zhang Ruoxue; Mahadevan, Sankaran
2003-01-01
This paper develops a methodology to assess the reliability computation model validity using the concept of Bayesian hypothesis testing, by comparing the model prediction and experimental observation, when there is only one computational model available to evaluate system behavior. Time-independent and time-dependent problems are investigated, with consideration of both cases: with and without statistical uncertainty in the model. The case of time-independent failure probability prediction with no statistical uncertainty is a straightforward application of Bayesian hypothesis testing. However, for the life prediction (time-dependent reliability) problem, a new methodology is developed in this paper to make the same Bayesian hypothesis testing concept applicable. With the existence of statistical uncertainty in the model, in addition to the application of a predictor estimator of the Bayes factor, the uncertainty in the Bayes factor is explicitly quantified through treating it as a random variable and calculating the probability that it exceeds a specified value. The developed method provides a rational criterion to decision-makers for the acceptance or rejection of the computational model
Risk-sensitivity in Bayesian sensorimotor integration.
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Jordi Grau-Moya
Full Text Available Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.
Bayesian outcome-based strategy classification.
Lee, Michael D
2016-03-01
Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014) recently developed a method for making inferences about the decision processes people use in multi-attribute forced choice tasks. Their paper makes a number of worthwhile theoretical and methodological contributions. Theoretically, they provide an insightful psychological motivation for a probabilistic extension of the widely-used "weighted additive" (WADD) model, and show how this model, as well as other important models like "take-the-best" (TTB), can and should be expressed in terms of meaningful priors. Methodologically, they develop an inference approach based on the Minimum Description Length (MDL) principles that balances both the goodness-of-fit and complexity of the decision models they consider. This paper aims to preserve these useful contributions, but provide a complementary Bayesian approach with some theoretical and methodological advantages. We develop a simple graphical model, implemented in JAGS, that allows for fully Bayesian inferences about which models people use to make decisions. To demonstrate the Bayesian approach, we apply it to the models and data considered by Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014), showing how a prior predictive analysis of the models, and posterior inferences about which models people use and the parameter settings at which they use them, can contribute to our understanding of human decision making.
Bayesian Methods for Radiation Detection and Dosimetry
International Nuclear Information System (INIS)
Peter G. Groer
2002-01-01
We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed compartmental activities. From the estimated probability densities of the model parameters we were able to derive the densities for compartmental activities for a two compartment catenary model at different times. We also calculated the average activities and their standard deviation for a simple two compartment model
On Bayesian treatment of systematic uncertainties in confidence interval calculation
Tegenfeldt, Fredrik
2005-01-01
In high energy physics, a widely used method to treat systematic uncertainties in confidence interval calculations is based on combining a frequentist construction of confidence belts with a Bayesian treatment of systematic uncertainties. In this note we present a study of the coverage of this method for the standard Likelihood Ratio (aka Feldman & Cousins) construction for a Poisson process with known background and Gaussian or log-Normal distributed uncertainties in the background or signal efficiency. For uncertainties in the signal efficiency of upto 40 % we find over-coverage on the level of 2 to 4 % depending on the size of uncertainties and the region in signal space. Uncertainties in the background generally have smaller effect on the coverage. A considerable smoothing of the coverage curves is observed. A software package is presented which allows fast calculation of the confidence intervals for a variety of assumptions on shape and size of systematic uncertainties for different nuisance paramete...
Bayesian methods in the search for MH370
Davey, Sam; Holland, Ian; Rutten, Mark; Williams, Jason
2016-01-01
This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was used to define the search zone in the southern Indian Ocean. The book describes particle-filter based numerical calculation of the aircraft flight-path probability distribution and validates the method using data from several of the involved aircraft’s previous flights. Finally it is shown how the Reunion Island flaperon debris find affects the search probability distribution.
Bayesians versus frequentists a philosophical debate on statistical reasoning
Vallverdú, Jordi
2016-01-01
This book analyzes the origins of statistical thinking as well as its related philosophical questions, such as causality, determinism or chance. Bayesian and frequentist approaches are subjected to a historical, cognitive and epistemological analysis, making it possible to not only compare the two competing theories, but to also find a potential solution. The work pursues a naturalistic approach, proceeding from the existence of numerosity in natural environments to the existence of contemporary formulas and methodologies to heuristic pragmatism, a concept introduced in the book’s final section. This monograph will be of interest to philosophers and historians of science and students in related fields. Despite the mathematical nature of the topic, no statistical background is required, making the book a valuable read for anyone interested in the history of statistics and human cognition.
Optimization of plasma diagnostics using Bayesian probability theory
International Nuclear Information System (INIS)
Dreier, H.; Dinklage, A.; Hirsch, M.; Kornejew, P.; Fischer, R.
2006-01-01
The diagnostic set-up for Wendelstein 7-X, a magnetic fusion device presently under construction, is currently in the design process to optimize the outcome under given technical constraints. Compared to traditional design approaches, Bayesian Experimental Design (BED) allows to optimize with respect to physical motivated design criterions. It aims to find the optimal design by maximizing an expected utility function that quantifies the goals of the experiment. The expectation marginalizes over the uncertain physical parameters and the possible values of future data. The approach presented here bases on maximization of an information measure (Kullback-Leibler entropy). As an example, the optimization of an infrared multichannel interferometer is shown in detail. Design aspects like the impact of technical restrictions are discussed
Designing and testing inflationary models with Bayesian networks
Energy Technology Data Exchange (ETDEWEB)
Price, Layne C. [Carnegie Mellon Univ., Pittsburgh, PA (United States). Dept. of Physics; Auckland Univ. (New Zealand). Dept. of Physics; Peiris, Hiranya V. [Univ. College London (United Kingdom). Dept. of Physics and Astronomy; Frazer, Jonathan [DESY Hamburg (Germany). Theory Group; Univ. of the Basque Country, Bilbao (Spain). Dept. of Theoretical Physics; Basque Foundation for Science, Bilbao (Spain). IKERBASQUE; Easther, Richard [Auckland Univ. (New Zealand). Dept. of Physics
2015-11-15
Even simple inflationary scenarios have many free parameters. Beyond the variables appearing in the inflationary action, these include dynamical initial conditions, the number of fields, and couplings to other sectors. These quantities are often ignored but cosmological observables can depend on the unknown parameters. We use Bayesian networks to account for a large set of inflationary parameters, deriving generative models for the primordial spectra that are conditioned on a hierarchical set of prior probabilities describing the initial conditions, reheating physics, and other free parameters. We use N{sub f}-quadratic inflation as an illustrative example, finding that the number of e-folds N{sub *} between horizon exit for the pivot scale and the end of inflation is typically the most important parameter, even when the number of fields, their masses and initial conditions are unknown, along with possible conditional dependencies between these parameters.
Designing and testing inflationary models with Bayesian networks
Energy Technology Data Exchange (ETDEWEB)
Price, Layne C. [McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213 (United States); Peiris, Hiranya V. [Department of Physics and Astronomy, University College London, London WC1E 6BT (United Kingdom); Frazer, Jonathan [Deutsches Elektronen-Synchrotron DESY, Theory Group, 22603 Hamburg (Germany); Easther, Richard, E-mail: laynep@andrew.cmu.edu, E-mail: h.peiris@ucl.ac.uk, E-mail: jonathan.frazer@desy.de, E-mail: r.easther@auckland.ac.nz [Department of Physics, University of Auckland, Private Bag 92019, Auckland (New Zealand)
2016-02-01
Even simple inflationary scenarios have many free parameters. Beyond the variables appearing in the inflationary action, these include dynamical initial conditions, the number of fields, and couplings to other sectors. These quantities are often ignored but cosmological observables can depend on the unknown parameters. We use Bayesian networks to account for a large set of inflationary parameters, deriving generative models for the primordial spectra that are conditioned on a hierarchical set of prior probabilities describing the initial conditions, reheating physics, and other free parameters. We use N{sub f}-quadratic inflation as an illustrative example, finding that the number of e-folds N{sub *} between horizon exit for the pivot scale and the end of inflation is typically the most important parameter, even when the number of fields, their masses and initial conditions are unknown, along with possible conditional dependencies between these parameters.
Designing and testing inflationary models with Bayesian networks
International Nuclear Information System (INIS)
Price, Layne C.; Auckland Univ.; Peiris, Hiranya V.; Frazer, Jonathan; Univ. of the Basque Country, Bilbao; Basque Foundation for Science, Bilbao; Easther, Richard
2015-11-01
Even simple inflationary scenarios have many free parameters. Beyond the variables appearing in the inflationary action, these include dynamical initial conditions, the number of fields, and couplings to other sectors. These quantities are often ignored but cosmological observables can depend on the unknown parameters. We use Bayesian networks to account for a large set of inflationary parameters, deriving generative models for the primordial spectra that are conditioned on a hierarchical set of prior probabilities describing the initial conditions, reheating physics, and other free parameters. We use N f -quadratic inflation as an illustrative example, finding that the number of e-folds N * between horizon exit for the pivot scale and the end of inflation is typically the most important parameter, even when the number of fields, their masses and initial conditions are unknown, along with possible conditional dependencies between these parameters.
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.
Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.
Directory of Open Access Journals (Sweden)
Xia Jiang
Full Text Available The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS datasets, which involve millions of single nucleotide polymorphism (SNPs, where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects.We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer's dataset, we investigated the performance of MBS-IGain.When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer's dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is significant because we have increasingly
Being Bayesian in a quantum world
International Nuclear Information System (INIS)
Fuchs, C.
2005-01-01
Full text: To be a Bayesian about probability theory is to accept that probabilities represent subjective degrees of belief and nothing more. This is in distinction to the idea that probabilities represent long-term frequencies or objective propensities. But, how can a subjective account of probabilities coexist with the existence of quantum mechanics? To accept quantum mechanics is to accept the calculational apparatus of quantum states and the Born rule for determining probabilities in a quantum measurement. If there ever were a place for probabilities to be objective, it ought to be here. This raises the question of whether Bayesianism and quantum mechanics are compatible at all. For the Bayesian, it only suggests that we should rethink what quantum mechanics is about. Is it 'law of nature' or really more 'law of thought'? From transistors to lasers, the evidence is in that we live in a quantum world. One could infer from this that all the elements in the quantum formalism necessarily mirror nature itself: wave functions are so successful as calculational tools precisely because they represent elements of reality. A more Bayesian-like perspective is that if wave functions generate probabilities, then they too must be Bayesian degrees of belief, with all that such a radical idea entails. In particular, quantum probabilities have no firmer hold on reality than the word 'belief' in 'degrees of belief' already indicates. From this perspective, the only sense in which the quantum formalism mirrors nature is through the constraints it places on gambling agents who would like to better navigate through world. One might think that this is thin information, but it is not insubstantial. To the extent that an agent should use quantum mechanics for his uncertainty accounting rather than some other theory tells us something about the world itself - i.e., the world independent of the agent and his particular beliefs at any moment. In this talk, I will try to shore up these
Directory of Open Access Journals (Sweden)
Erik Ersmark
2016-12-01
Full Text Available The global distribution of the grey wolf (Canis lupus is a complex assembly consisting of a large number of populations and described subspecies. How these lineages are related to one another is still not fully resolved, largely due to the fact that large geographical regions remain poorly sampled both at the core and periphery of the species’ range. Analyses of ancient wolves have also suffered from uneven sampling, but have shown indications of a major turnover at some point during the Pleistocene-Holocene boundary in northern North America. Here we analyze variation in the mitochondrial control region in 122 contemporary wolves from some of the less studied populations, as well as six samples from the previously unstudied Greenland subspecies (Canis l. orion and two Late Pleistocene samples from Siberia. Together with the publicly available control region sequences of both modern and ancient wolves, this study examines genetic diversity on a wide geographical and temporal scale that includes both Eurasia and North America. We identify 13 new haplotypes, of which the majority is found in northern and eastern Asia. The results show that the Greenland samples are all represented by one haplotype, previously identified in North American wolves, among which this population seems to trace its maternal lineage. The phylogeny and network analyses show a wide spatial distribution of several lineages, but also some clusters with more distinct geographical affiliation. In North America, we find support for an end-Pleistocene population bottleneck through coalescent simulations under an approximate Bayesian framework in contrast to previous studies that suggested an extinction-replacement event. However, we find no support for a similar bottleneck in Eurasia. Overall, this global analysis helps to clarify our understanding of the complex history for wolves in Eurasia and North America.
Learning Local Components to Understand Large Bayesian Networks
DEFF Research Database (Denmark)
Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge
2009-01-01
(domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....
An introduction to using Bayesian linear regression with clinical data.
Baldwin, Scott A; Larson, Michael J
2017-11-01
Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.
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...
Bayesian non- and semi-parametric methods and applications
Rossi, Peter
2014-01-01
This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number
Sparse Event Modeling with Hierarchical Bayesian Kernel Methods
2016-01-05
SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function
Learning Bayesian Networks with Incomplete Data by Augmentation
Adel, Tameem; de Campos, Cassio P.
2016-01-01
We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...
Bayesian emulation for optimization in multi-step portfolio decisions
Irie, Kaoru; West, Mike
2016-01-01
We discuss the Bayesian emulation approach to computational solution of multi-step portfolio studies in financial time series. "Bayesian emulation for decisions" involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portf...
Directory of Open Access Journals (Sweden)
Joanna Zigouris
Full Text Available Interglacial-glacial cycles of the Quaternary are widely recognized in shaping phylogeographic structure. Patterns from cold adapted species can be especially informative - in particular, uncovering additional glacial refugia, identifying likely recolonization patterns, and increasing our understanding of species' responses to climate change. We investigated phylogenetic structure of the wolverine, a wide-ranging cold adapted carnivore, using a 318 bp of the mitochondrial DNA control region for 983 wolverines (n=209 this study, n=774 from GenBank from across their full Holarctic distribution. Bayesian phylogenetic tree reconstruction and the distribution of observed pairwise haplotype differences (mismatch distribution provided evidence of a single rapid population expansion across the wolverine's Holarctic range. Even though molecular evidence corroborated a single refugium, significant subdivisions of population genetic structure (0.01< ΦST <0.99, P<0.05 were detected. Pairwise ΦST estimates separated Scandinavia from Russia and Mongolia, and identified five main divisions within North America - the Central Arctic, a western region, an eastern region consisting of Ontario and Quebec/Labrador, Manitoba, and California. These data are in contrast to the nearly panmictic structure observed in northwestern North America using nuclear microsatellites, but largely support the nuclear DNA separation of contemporary Manitoba and Ontario wolverines from northern populations. Historic samples (c. 1900 from the functionally extirpated eastern population of Quebec/Labrador displayed genetic similarities to contemporary Ontario wolverines. To understand these divergence patterns, four hypotheses were tested using Approximate Bayesian Computation (ABC. The most supported hypothesis was a single Beringia incursion during the last glacial maximum that established the northwestern population, followed by a west-to-east colonization during the Holocene. This
Zigouris, Joanna; Schaefer, James A; Fortin, Clément; Kyle, Christopher J
2013-01-01
Interglacial-glacial cycles of the Quaternary are widely recognized in shaping phylogeographic structure. Patterns from cold adapted species can be especially informative - in particular, uncovering additional glacial refugia, identifying likely recolonization patterns, and increasing our understanding of species' responses to climate change. We investigated phylogenetic structure of the wolverine, a wide-ranging cold adapted carnivore, using a 318 bp of the mitochondrial DNA control region for 983 wolverines (n=209 this study, n=774 from GenBank) from across their full Holarctic distribution. Bayesian phylogenetic tree reconstruction and the distribution of observed pairwise haplotype differences (mismatch distribution) provided evidence of a single rapid population expansion across the wolverine's Holarctic range. Even though molecular evidence corroborated a single refugium, significant subdivisions of population genetic structure (0.01< ΦST <0.99, P<0.05) were detected. Pairwise ΦST estimates separated Scandinavia from Russia and Mongolia, and identified five main divisions within North America - the Central Arctic, a western region, an eastern region consisting of Ontario and Quebec/Labrador, Manitoba, and California. These data are in contrast to the nearly panmictic structure observed in northwestern North America using nuclear microsatellites, but largely support the nuclear DNA separation of contemporary Manitoba and Ontario wolverines from northern populations. Historic samples (c. 1900) from the functionally extirpated eastern population of Quebec/Labrador displayed genetic similarities to contemporary Ontario wolverines. To understand these divergence patterns, four hypotheses were tested using Approximate Bayesian Computation (ABC). The most supported hypothesis was a single Beringia incursion during the last glacial maximum that established the northwestern population, followed by a west-to-east colonization during the Holocene. This pattern is
Risks Analysis of Logistics Financial Business Based on Evidential Bayesian Network
Directory of Open Access Journals (Sweden)
Ying Yan
2013-01-01
Full Text Available Risks in logistics financial business are identified and classified. Making the failure of the business as the root node, a Bayesian network is constructed to measure the risk levels in the business. Three importance indexes are calculated to find the most important risks in the business. And more, considering the epistemic uncertainties in the risks, evidence theory associate with Bayesian network is used as an evidential network in the risk analysis of logistics finance. To find how much uncertainty in root node is produced by each risk, a new index, epistemic importance, is defined. Numerical examples show that the proposed methods could provide a lot of useful information. With the information, effective approaches could be found to control and avoid these sensitive risks, thus keep logistics financial business working more reliable. The proposed method also gives a quantitative measure of risk levels in logistics financial business, which provides guidance for the selection of financing solutions.
DEFF Research Database (Denmark)
Dobat, Andres S.
2016-01-01
In 2003, a hitherto unknown Viking age settlement was discovered at Füsing in Northern Germany close to Hedeby/Schleswig, the largest of the early Scandinavian towns. Finds and building features suggest a high status residence and a seat of some chiefly elite that flourished from around 700 to th...... and the transformation of socio‐political structures in Northern Europe as it transitioned from prehistory into the middle Ages....
Doing bayesian data analysis a tutorial with R and BUGS
Kruschke, John K
2011-01-01
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all
A Bayesian Justification for Random Sampling in Sample Survey
Directory of Open Access Journals (Sweden)
Glen Meeden
2012-07-01
Full Text Available In the usual Bayesian approach to survey sampling the sampling design, plays a minimal role, at best. Although a close relationship between exchangeable prior distributions and simple random sampling has been noted; how to formally integrate simple random sampling into the Bayesian paradigm is not clear. Recently it has been argued that the sampling design can be thought of as part of a Bayesian's prior distribution. We will show here that under this scenario simple random sample can be given a Bayesian justification in survey sampling.
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...
Uncertainty measurement with belief entropy on interference effect in Quantum-Like Bayesian Networks
Huang, Zhiming; Yang, Lin; Jiang, Wen
2017-01-01
Social dilemmas have been regarded as the essence of evolution game theory, in which the prisoner's dilemma game is the most famous metaphor for the problem of cooperation. Recent findings revealed people's behavior violated the Sure Thing Principle in such games. Classic probability methodologies have difficulty explaining the underlying mechanisms of people's behavior. In this paper, a novel quantum-like Bayesian Network was proposed to accommodate the paradoxical phenomenon. The special ne...
Assessing global vegetation activity using spatio-temporal Bayesian modelling
Mulder, Vera L.; van Eck, Christel M.; Friedlingstein, Pierre; Regnier, Pierre A. G.
2016-04-01
This work demonstrates the potential of modelling vegetation activity using a hierarchical Bayesian spatio-temporal model. This approach allows modelling changes in vegetation and climate simultaneous in space and time. Changes of vegetation activity such as phenology are modelled as a dynamic process depending on climate variability in both space and time. Additionally, differences in observed vegetation status can be contributed to other abiotic ecosystem properties, e.g. soil and terrain properties. Although these properties do not change in time, they do change in space and may provide valuable information in addition to the climate dynamics. The spatio-temporal Bayesian models were calibrated at a regional scale because the local trends in space and time can be better captured by the model. The regional subsets were defined according to the SREX segmentation, as defined by the IPCC. Each region is considered being relatively homogeneous in terms of large-scale climate and biomes, still capturing small-scale (grid-cell level) variability. Modelling within these regions is hence expected to be less uncertain due to the absence of these large-scale patterns, compared to a global approach. This overall modelling approach allows the comparison of model behavior for the different regions and may provide insights on the main dynamic processes driving the interaction between vegetation and climate within different regions. The data employed in this study encompasses the global datasets for soil properties (SoilGrids), terrain properties (Global Relief Model based on SRTM DEM and ETOPO), monthly time series of satellite-derived vegetation indices (GIMMS NDVI3g) and climate variables (Princeton Meteorological Forcing Dataset). The findings proved the potential of a spatio-temporal Bayesian modelling approach for assessing vegetation dynamics, at a regional scale. The observed interrelationships of the employed data and the different spatial and temporal trends support
Wallace, Lisa E; Wheeler, Gregory L; McGlaughlin, Mitchell E; Bresowar, Gerald; Helenurm, Kaius
2017-05-01
Taxa inhabiting the California Channel Islands exhibit variation in their degree of isolation, but few studies have considered patterns across the entire archipelago. We studied phylogeography of insular Acmispon argophyllus and A. dendroideus to determine whether infraspecific taxa are genetically divergent and to elucidate patterns of diversification across these islands. DNA sequences were collected from nuclear (ADH) and plastid genomes ( rpL16 , ndhA , psbD-trnT ) from >450 samples on the Channel Islands and California. We estimated population genetic diversity and structure, phylogenetic patterns among populations, and migration rates, and tested for population growth. Populations of northern island A. argophyllus var. niveus are genetically distinct from conspecific populations on southern islands. On the southern islands, A. argophyllus var. argenteus populations on Santa Catalina are phylogenetically distinct from populations of var. argenteus and var. adsurgens on the other southern islands. For A. dendroideus , we found the varieties to be monophyletic. Populations of A. dendroideus var. traskiae on San Clemente are genetically differentiated from other conspecific populations, whereas populations on the northern islands and Santa Catalina show varying degrees of gene flow. Evidence of population growth was found in both species. Oceanic barriers between islands have had a strong influence on population genetic structure in both Acmispon species, although the species have differing phylogeographic patterns. This study provides a contrasting pattern of dispersal on a near island system that does not follow a strict stepping-stone model, commonly found on isolated island systems. © 2017 Botanical Society of America.
Directory of Open Access Journals (Sweden)
Maggie CY Lau
2014-10-01
Full Text Available Comparative studies on community phylogenetics and phylogeography of microorganisms living in extreme environments are rare. Terrestrial subsurface habitats are valuable for studying microbial biogeographical patterns due to their isolation and the restricted dispersal mechanisms. Since the taxonomic identity of a microorganism does not always correspond well with its functional role in a particular community, the use of taxonomic assignments or patterns may give limited inference on how microbial functions are affected by historical, geographical and environmental factors. With seven metagenomic libraries generated from fracture water samples collected from five South African mines, this study was carried out to (1 screen for ubiquitous functions or pathways of biogeochemical cycling of CH4, S and N; (2 to characterize the biodiversity represented by the common functional genes; (3 to investigate the subsurface biogeography as revealed by this subset of genes; and (4 to explore the possibility of using metagenomic data for evolutionary study. The ubiquitous functional genes are NarV, NPD, PAP reductase, NifH, NifD, NifK, NifE and NifN genes. Although these 8 common functional genes were taxonomically and phylogenetically diverse and distinct from each other, the dissimilarity between samples did not correlate strongly with either geographical, environmental or residence time of the water. Por genes homologous to those of Thermodesulfovibrio yellowstonii detected in all metagenomes were deep lineages of Nitrospirae, suggesting that subsurface habitats have preserved ancestral genetic signatures that inform the study of the origin and evolution of prokaryotes.
Pyron, R. Alexander; Peñafiel, Nicolás; Romero-Barreto, Paulina; Culebras, Jaime; Bustamante, Lucas; Yánez-Muñoz, Mario H.; Guayasamin, Juan M.
2016-01-01
Comparative phylogeography allow us to understand how shared historical circumstances have shaped the formation of lineages, by examining a broad spectrum of co-distributed populations of different taxa. However, these types of studies are scarce in the Neotropics, a region that is characterized by high diversity, complex geology, and poorly understood biogeography. Here, we investigate the diversification patterns of five lineages of amphibians and reptiles, co-distributed across the Choco and Andes ecoregions in northwestern Ecuador. Mitochondrial DNA and occurrence records were used to determine the degree of geographic genetic divergence within species. Our results highlight congruent patterns of parapatric speciation and common geographical barriers for distantly related taxa. These comparisons indicate similar biological and demographic characteristics for the included clades, and reveal the existence of two new species of Pristimantis previously subsumed under P. walkeri, which we describe herein. Our data supports the hypothesis that widely distributed Chocoan taxa may generally experience their greatest opportunities for isolation and parapatric speciation across thermal elevational gradients. Finally, our study provides critical information to predict which unstudied lineages may harbor cryptic diversity, and how geology and climate are likely to have shaped their evolutionary history. PMID:27120100
Arteaga, Alejandro; Pyron, R Alexander; Peñafiel, Nicolás; Romero-Barreto, Paulina; Culebras, Jaime; Bustamante, Lucas; Yánez-Muñoz, Mario H; Guayasamin, Juan M
2016-01-01
Comparative phylogeography allow us to understand how shared historical circumstances have shaped the formation of lineages, by examining a broad spectrum of co-distributed populations of different taxa. However, these types of studies are scarce in the Neotropics, a region that is characterized by high diversity, complex geology, and poorly understood biogeography. Here, we investigate the diversification patterns of five lineages of amphibians and reptiles, co-distributed across the Choco and Andes ecoregions in northwestern Ecuador. Mitochondrial DNA and occurrence records were used to determine the degree of geographic genetic divergence within species. Our results highlight congruent patterns of parapatric speciation and common geographical barriers for distantly related taxa. These comparisons indicate similar biological and demographic characteristics for the included clades, and reveal the existence of two new species of Pristimantis previously subsumed under P. walkeri, which we describe herein. Our data supports the hypothesis that widely distributed Chocoan taxa may generally experience their greatest opportunities for isolation and parapatric speciation across thermal elevational gradients. Finally, our study provides critical information to predict which unstudied lineages may harbor cryptic diversity, and how geology and climate are likely to have shaped their evolutionary history.
Latch, Emily K; Heffelfinger, James R; Fike, Jennifer A; Rhodes, Olin E
2009-04-01
Quaternary climatic oscillations greatly influenced the present-day population genetic structure of animals and plants. For species with high dispersal and reproductive potential, phylogeographic patterns resulting from historical processes can be cryptic, overshadowed by contemporary processes. Here we report a study of the phylogeography of Odocoileus hemionus, a large, vagile ungulate common throughout western North America. We examined sequence variation of mitochondrial DNA (control region and cytochrome b) within and among 70 natural populations across the entire range of the species. Among the 1766 individual animals surveyed, we recovered 496 haplotypes. Although fine-scale phylogenetic structure was weakly resolved using phylogenetic methods, network analysis clearly revealed the presence of 12 distinct haplogroups. The spatial distribution of haplogroups showed a strong genetic discontinuity between the two morphological types of O. hemionus, mule deer and black-tailed deer, east and west of the Cascade Mountains in the Pacific Northwest. Within the mule deer lineage, we identified several haplogroups that expanded before or during the Last Glacial Maximum, suggesting that mule deer persisted in multiple refugia south of the ice sheets. Patterns of genetic diversity within the black-tailed deer lineage suggest a single refugium along the Pacific Northwest coast, and refute the hypothesis that black-tailed deer persisted in one or more northern refugia. Our data suggest that black-tailed deer recolonized areas in accordance with the pattern of glacial retreat, with initial recolonization northward along a coastal route and secondary recolonization inland.
Directory of Open Access Journals (Sweden)
Lisa M Nigro
2016-08-01
Full Text Available Deep-sea hypersaline anoxic basins (DHABs and other hypersaline environments contain abundant and diverse microbial life that has adapted to these extreme conditions. The bacterial Candidate Division KB1 represents one of several uncultured groups that has been consistently observed in hypersaline microbial diversity studies. Here we report the phylogeography of KB1, its phylogenetic relationships to Candidate Division OP1 Bacteria, and its potential metabolic and osmotic stress adaptations based on a partial single cell amplified genome (SAG of KB1 from Orca Basin, the largest hypersaline seafloor brine basin in the Gulf of Mexico. Our results are consistent with the hypothesis – previously developed based on 14C incorporation experiments with mixed-species enrichments from Mediterranean seafloor brines - that KB1 has adapted its proteins to elevated intracellular salinity, but at the same time KB1 apparently imports glycine betaine; this compatible solute is potentially not limited to osmoregulation but could also serve as a carbon and energy source.
The frequentist implications of optional stopping on Bayesian hypothesis tests.
Sanborn, Adam N; Hills, Thomas T
2014-04-01
Null hypothesis significance testing (NHST) is the most commonly used statistical methodology in psychology. The probability of achieving a value as extreme or more extreme than the statistic obtained from the data is evaluated, and if it is low enough, the null hypothesis is rejected. However, because common experimental practice often clashes with the assumptions underlying NHST, these calculated probabilities are often incorrect. Most commonly, experimenters use tests that assume that sample sizes are fixed in advance of data collection but then use the data to determine when to stop; in the limit, experimenters can use data monitoring to guarantee that the null hypothesis will be rejected. Bayesian hypothesis testing (BHT) provides a solution to these ills because the stopping rule used is irrelevant to the calculation of a Bayes factor. In addition, there are strong mathematical guarantees on the frequentist properties of BHT that are comforting for researchers concerned that stopping rules could influence the Bayes factors produced. Here, we show that these guaranteed bounds have limited scope and often do not apply in psychological research. Specifically, we quantitatively demonstrate the impact of optional stopping on the resulting Bayes factors in two common situations: (1) when the truth is a combination of the hypotheses, such as in a heterogeneous population, and (2) when a hypothesis is composite-taking multiple parameter values-such as the alternative hypothesis in a t-test. We found that, for these situations, while the Bayesian interpretation remains correct regardless of the stopping rule used, the choice of stopping rule can, in some situations, greatly increase the chance of experimenters finding evidence in the direction they desire. We suggest ways to control these frequentist implications of stopping rules on BHT.
Development of a cyber security risk model using Bayesian networks
International Nuclear Information System (INIS)
Shin, Jinsoo; Son, Hanseong; Khalil ur, Rahman; Heo, Gyunyoung
2015-01-01
Cyber security is an emerging safety issue in the nuclear industry, especially in the instrumentation and control (I and C) field. To address the cyber security issue systematically, a model that can be used for cyber security evaluation is required. In this work, a cyber security risk model based on a Bayesian network is suggested for evaluating cyber security for nuclear facilities in an integrated manner. The suggested model enables the evaluation of both the procedural and technical aspects of cyber security, which are related to compliance with regulatory guides and system architectures, respectively. The activity-quality analysis model was developed to evaluate how well people and/or organizations comply with the regulatory guidance associated with cyber security. The architecture analysis model was created to evaluate vulnerabilities and mitigation measures with respect to their effect on cyber security. The two models are integrated into a single model, which is called the cyber security risk model, so that cyber security can be evaluated from procedural and technical viewpoints at the same time. The model was applied to evaluate the cyber security risk of the reactor protection system (RPS) of a research reactor and to demonstrate its usefulness and feasibility. - Highlights: • We developed the cyber security risk model can be find the weak point of cyber security integrated two cyber analysis models by using Bayesian Network. • One is the activity-quality model signifies how people and/or organization comply with the cyber security regulatory guide. • Other is the architecture model represents the probability of cyber-attack on RPS architecture. • The cyber security risk model can provide evidence that is able to determine the key element for cyber security for RPS of a research reactor
Bayesian network models for error detection in radiotherapy plans
International Nuclear Information System (INIS)
Kalet, Alan M; Ford, Eric C; Phillips, Mark H; Gennari, John H
2015-01-01
The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network’s conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures. (paper)
Internal Dosimetry Intake Estimation using Bayesian Methods
International Nuclear Information System (INIS)
Miller, G.; Inkret, W.C.; Martz, H.F.
1999-01-01
New methods for the inverse problem of internal dosimetry are proposed based on evaluating expectations of the Bayesian posterior probability distribution of intake amounts, given bioassay measurements. These expectation integrals are normally of very high dimension and hence impractical to use. However, the expectations can be algebraically transformed into a sum of terms representing different numbers of intakes, with a Poisson distribution of the number of intakes. This sum often rapidly converges, when the average number of intakes for a population is small. A simplified algorithm using data unfolding is described (UF code). (author)
Bayesian approach to inverse statistical mechanics
Habeck, Michael
2014-05-01
Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.
On local optima in learning bayesian networks
DEFF Research Database (Denmark)
Dalgaard, Jens; Kocka, Tomas; Pena, Jose
2003-01-01
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness...... is set at maximum, KES corresponds to the greedy equivalence search algorithm (GES). When greediness is kept at minimum, we prove that under mild assumptions KES asymptotically returns any inclusion optimal BN with nonzero probability. Experimental results for both synthetic and real data are reported...
A Bayesian concept learning approach to crowdsourcing
DEFF Research Database (Denmark)
Viappiani, P.; Zilles, S.; Hamilton, H.J.
2011-01-01
techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing......We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation...
Default Bayesian Estimation of the Fundamental Frequency
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2013-01-01
Joint fundamental frequency and model order esti- mation is an important problem in several applications. In this paper, a default estimation algorithm based on a minimum of prior information is presented. The algorithm is developed in a Bayesian framework, and it can be applied to both real....... Moreover, several approximations of the posterior distributions on the fundamental frequency and the model order are derived, and one of the state-of-the-art joint fundamental frequency and model order estimators is demonstrated to be a special case of one of these approximations. The performance...
Bayesian regression of piecewise homogeneous Poisson processes
Directory of Open Access Journals (Sweden)
Diego Sevilla
2015-12-01
Full Text Available In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data. The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes. Received: 2 November 2015, Accepted: 27 November 2015; Edited by: R. Dickman; Reviewed by: M. Hutter, Australian National University, Canberra, Australia.; DOI: http://dx.doi.org/10.4279/PIP.070018 Cite as: D J R Sevilla, Papers in Physics 7, 070018 (2015
Structure-based bayesian sparse reconstruction
Quadeer, Ahmed Abdul
2012-12-01
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.
Bayesian regularization of diffusion tensor images
DEFF Research Database (Denmark)
Frandsen, Jesper; Hobolth, Asger; Østergaard, Leif
2007-01-01
Diffusion tensor imaging (DTI) is a powerful tool in the study of the course of nerve fibre bundles in the human brain. Using DTI, the local fibre orientation in each image voxel can be described by a diffusion tensor which is constructed from local measurements of diffusion coefficients along...... several directions. The measured diffusion coefficients and thereby the diffusion tensors are subject to noise, leading to possibly flawed representations of the three dimensional fibre bundles. In this paper we develop a Bayesian procedure for regularizing the diffusion tensor field, fully utilizing...
Bayesian estimation of core-melt probability
International Nuclear Information System (INIS)
Lewis, H.W.
1984-01-01
A very simple application of the canonical Bayesian algorithm is made to the problem of estimation of the probability of core melt in a commercial power reactor. An approximation to the results of the Rasmussen study on reactor safety is used as the prior distribution, and the observation that there has been no core melt yet is used as the single experiment. The result is a substantial decrease in the mean probability of core melt--factors of 2 to 4 for reasonable choices of parameters. The purpose is to illustrate the procedure, not to argue for the decrease
Optimized Bayesian dynamic advising theory and algorithms
Karny, Miroslav
2006-01-01
Written by one of the world's leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modelling by dynamic mixture models
Bayesian Predictive Models for Rayleigh Wind Speed
DEFF Research Database (Denmark)
Shahirinia, Amir; Hajizadeh, Amin; Yu, David C
2017-01-01
predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior...... and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented....
Bayesian stratified sampling to assess corpus utility
Energy Technology Data Exchange (ETDEWEB)
Hochberg, J.; Scovel, C.; Thomas, T.; Hall, S.
1998-12-01
This paper describes a method for asking statistical questions about a large text corpus. The authors exemplify the method by addressing the question, ``What percentage of Federal Register documents are real documents, of possible interest to a text researcher or analyst?`` They estimate an answer to this question by evaluating 200 documents selected from a corpus of 45,820 Federal Register documents. Bayesian analysis and stratified sampling are used to reduce the sampling uncertainty of the estimate from over 3,100 documents to fewer than 1,000. A possible application of the method is to establish baseline statistics used to estimate recall rates for information retrieval systems.
Personalized Audio Systems - a Bayesian Approach
DEFF Research Database (Denmark)
Nielsen, Jens Brehm; Jensen, Bjørn Sand; Hansen, Toke Jansen
2013-01-01
Modern audio systems are typically equipped with several user-adjustable parameters unfamiliar to most users listening to the system. To obtain the best possible setting, the user is forced into multi-parameter optimization with respect to the users's own objective and preference. To address this......, the present paper presents a general inter-active framework for personalization of such audio systems. The framework builds on Bayesian Gaussian process regression in which a model of the users's objective function is updated sequentially. The parameter setting to be evaluated in a given trial is selected...
Kuch, Ulrich; Keogh, J. Scott; Weigel, John; Smith, Laurie A.; Mebs, Dietrich
2005-03-01
King brown snakes or mulga snakes (Pseudechis australis) are the largest and among the most dangerous and wide-ranging venomous snakes in Australia and New Guinea. They occur in diverse habitats, are important predators, and exhibit considerable morphological variation. We infer the relationships and historical biogeography of P. australis based on phylogenetic analysis of 1,249 base pairs from the mitochondrial cytochrome b, NADH dehydrogenase subunit 4 and three adjacent tRNA genes using Bayesian, maximum-likelihood, and maximum-parsimony methods. All methods reveal deep phylogenetic structure with four strongly supported clades comprising snakes from New Guinea (I), localities all over Australia (II), the Kimberleys of Western Australia (III), and north-central Australia (IV), suggesting a much more ancient radiation than previously believed. This conclusion is robust to different molecular clock estimations indicating divergence in Pliocene or Late Miocene, after landbridge dispersal to New Guinea had occurred. While members of clades I, III and IV are medium-sized, slender snakes, those of clade II attain large sizes and a robust build, rendering them top predators in their ecosystems. Genetic differentiation within clade II is low and haplotype distribution largely incongruent with geography or colour morphs, suggesting Pleistocene dispersal and recent ecomorph evolution. Significant haplotype diversity exists in clades III and IV, implying that clade IV comprises two species. Members of clade II are broadly sympatric with members of both northern Australian clades. Thus, our data support the recognition of at least five species from within P. australis (auct.) under various criteria. We discuss biogeographical, ecological and medical implications of our findings.
2D Bayesian automated tilted-ring fitting of disc galaxies in large H I galaxy surveys: 2DBAT
Oh, Se-Heon; Staveley-Smith, Lister; Spekkens, Kristine; Kamphuis, Peter; Koribalski, Bärbel S.
2018-01-01
We present a novel algorithm based on a Bayesian method for 2D tilted-ring analysis of disc galaxy velocity fields. Compared to the conventional algorithms based on a chi-squared minimization procedure, this new Bayesian-based algorithm suffers less from local minima of the model parameters even with highly multimodal posterior distributions. Moreover, the Bayesian analysis, implemented via Markov Chain Monte Carlo sampling, only requires broad ranges of posterior distributions of the parameters, which makes the fitting procedure fully automated. This feature will be essential when performing kinematic analysis on the large number of resolved galaxies expected to be detected in neutral hydrogen (H I) surveys with the Square Kilometre Array and its pathfinders. The so-called 2D Bayesian Automated Tilted-ring fitter (2DBAT) implements Bayesian fits of 2D tilted-ring models in order to derive rotation curves of galaxies. We explore 2DBAT performance on (a) artificial H I data cubes built based on representative rotation curves of intermediate-mass and massive spiral galaxies, and (b) Australia Telescope Compact Array H I data from the Local Volume H I Survey. We find that 2DBAT works best for well-resolved galaxies with intermediate inclinations (20° < i < 70°), complementing 3D techniques better suited to modelling inclined galaxies.
Directory of Open Access Journals (Sweden)
Kelemen Arpad
2008-08-01
Full Text Available Abstract Background This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug. Results and conclusion Bayesian categorical model for estimating the proportion of the 'call' are used for pre-screening genes. Hierarchical Bayesian Mixture Model is further developed for the identifications of differentially expressed genes across time and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. Bayesian mixture model produces the gene-specific posterior probability of differential/non-differential expression and the 95% credible interval, which is the basis for our further Bayesian meta-inference. Meta-analysis is performed in order to identify commonly expressed genes from multiple tissues that may serve as ideal targets for novel treatment strategies and to integrate the results across separate studies. We have found the common expressed genes in the three tissues. However, the up/down/no regulations of these common genes are different at different time points. Moreover, the most differentially expressed genes were found in the liver, then in kidney, and then in muscle.
Mechanistic curiosity will not kill the Bayesian cat
Borsboom, D.; Wagenmakers, E.-J.; Romeijn, J.-W.
2011-01-01
Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer
Mechanistic curiosity will not kill the Bayesian cat
Borsboom, Denny; Wagenmakers, Eric-Jan; Romeijn, Jan-Willem
Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer
A default Bayesian hypothesis test for correlations and partial correlations
Wetzels, R.; Wagenmakers, E.J.
2012-01-01
We propose a default Bayesian hypothesis test for the presence of a correlation or a partial correlation. The test is a direct application of Bayesian techniques for variable selection in regression models. The test is easy to apply and yields practical advantages that the standard frequentist tests
Applications of Bayesian decision theory to intelligent tutoring systems
Vos, Hendrik J.
1994-01-01
Some applications of Bayesian decision theory to intelligent tutoring systems are considered. How the problem of adapting the appropriate amount of instruction to the changing nature of a student's capabilities during the learning process can be situated in the general framework of Bayesian decision
Systematic search of Bayesian statistics in the field of psychotraumatology
van de Schoot, Rens; Schalken, Naomi; Olff, Miranda
2017-01-01
In many different disciplines there is a recent increase in interest of Bayesian analysis. Bayesian methods implement Bayes' theorem, which states that prior beliefs are updated with data, and this process produces updated beliefs about model parameters. The prior is based on how much information we
Power in Bayesian Mediation Analysis for Small Sample Research
Miočević, M.; MacKinnon, David; Levy, Roy
2017-01-01
Bayesian methods have the potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This article compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product,
An introduction to Bayesian statistics in health psychology
Depaoli, Sarah; Rus, Holly; Clifton, James; van de Schoot, A.G.J.; Tiemensma, Jitske
2017-01-01
The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of Health Psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation
Bayesian Estimation of Wave Spectra – Proper Formulation of ABIC
DEFF Research Database (Denmark)
Nielsen, Ulrik Dam
2007-01-01
It is possible to estimate on-site wave spectra using measured ship responses applied to Bayesian Modelling based on two prior information: the wave spectrum must be smooth both directional-wise and frequency-wise. This paper introduces two hyperparameters into Bayesian Modelling and, hence, a pr...
Using Alien Coins to Test Whether Simple Inference Is Bayesian
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…
Bayesian model ensembling using meta-trained recurrent neural networks
Ambrogioni, L.; Berezutskaya, Y.; Gü ç lü , U.; Borne, E.W.P. van den; Gü ç lü tü rk, Y.; Gerven, M.A.J. van; Maris, E.G.G.
2017-01-01
In this paper we demonstrate that a recurrent neural network meta-trained on an ensemble of arbitrary classification tasks can be used as an approximation of the Bayes optimal classifier. This result is obtained by relying on the framework of e-free approximate Bayesian inference, where the Bayesian
Estimating mental states of a depressed person with bayesian networks
Klein, Michel C.A.; Modena, Gabriele
2013-01-01
In this work in progress paper we present an approach based on Bayesian Networks to model the relationship between mental states and empirical observations in a depressed person. We encode relationships and domain expertise as a Hierarchical Bayesian Network. Mental states are represented as latent
Universal Darwinism As a Process of Bayesian Inference.
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.
Applying Bayesian Statistics to Educational Evaluation. Theoretical Paper No. 62.
Brumet, Michael E.
Bayesian statistical inference is unfamiliar to many educational evaluators. While the classical model is useful in educational research, it is not as useful in evaluation because of the need to identify solutions to practical problems based on a wide spectrum of information. The reason Bayesian analysis is effective for decision making is that it…
An introduction to Bayesian statistics in health psychology.
Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske
2017-09-01
The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.
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.
Non-homogeneous dynamic Bayesian networks for continuous data
Grzegorczyk, Marco; Husmeier, Dirk
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with
Bayesian Approaches to Imputation, Hypothesis Testing, and Parameter Estimation
Ross, Steven J.; Mackey, Beth
2015-01-01
This chapter introduces three applications of Bayesian inference to common and novel issues in second language research. After a review of the critiques of conventional hypothesis testing, our focus centers on ways Bayesian inference can be used for dealing with missing data, for testing theory-driven substantive hypotheses without a default null…
On Bayesian Inference under Sampling from Scale Mixtures of Normals
Fernández, C.; Steel, M.F.J.
1996-01-01
This paper considers a Bayesian analysis of the linear regression model under independent sampling from general scale mixtures of Normals.Using a common reference prior, we investigate the validity of Bayesian inference and the existence of posterior moments of the regression and precision
A Bayesian account of quantum histories
International Nuclear Information System (INIS)
Marlow, Thomas
2006-01-01
We investigate whether quantum history theories can be consistent with Bayesian reasoning and whether such an analysis helps clarify the interpretation of such theories. First, we summarise and extend recent work categorising two different approaches to formalising multi-time measurements in quantum theory. The standard approach consists of describing an ordered series of measurements in terms of history propositions with non-additive 'probabilities.' The non-standard approach consists of defining multi-time measurements to consist of sets of exclusive and exhaustive history propositions and recovering the single-time exclusivity of results when discussing single-time history propositions. We analyse whether such history propositions can be consistent with Bayes' rule. We show that certain class of histories are given a natural Bayesian interpretation, namely, the linearly positive histories originally introduced by Goldstein and Page. Thus, we argue that this gives a certain amount of interpretational clarity to the non-standard approach. We also attempt a justification of our analysis using Cox's axioms of probability theory
Adaptive Naive Bayesian Anti-Spam Engine
Gajewski, W P
2006-01-01
The problem of spam has been seriously troubling the Internet community during the last few years and currently reached an alarming scale. Observations made at CERN (European Organization for Nuclear Research located in Geneva, Switzerland) show that spam mails can constitute up to 75% of daily SMTP traffic. A naïve Bayesian classifier based on a Bag of Words representation of an email is widely used to stop this unwanted flood as it combines good performance with simplicity of the training and classification processes. However, facing the constantly changing patterns of spam, it is necessary to assure online adaptability of the classifier. This work proposes combining such a classifier with another NBC (naïve Bayesian classifier) based on pairs of adjacent words. Only the latter will be retrained with examples of spam reported by users. Tests are performed on considerable sets of mails both from public spam archives and CERN mailboxes. They suggest that this architecture can increase spam recall without af...
A Bayesian approach to person perception.
Clifford, C W G; Mareschal, I; Otsuka, Y; Watson, T L
2015-11-01
Here we propose a Bayesian approach to person perception, outlining the theoretical position and a methodological framework for testing the predictions experimentally. We use the term person perception to refer not only to the perception of others' personal attributes such as age and sex but also to the perception of social signals such as direction of gaze and emotional expression. The Bayesian approach provides a formal description of the way in which our perception combines current sensory evidence with prior expectations about the structure of the environment. Such expectations can lead to unconscious biases in our perception that are particularly evident when sensory evidence is uncertain. We illustrate the ideas with reference to our recent studies on gaze perception which show that people have a bias to perceive the gaze of others as directed towards themselves. We also describe a potential application to the study of the perception of a person's sex, in which a bias towards perceiving males is typically observed. Copyright © 2015 Elsevier Inc. All rights reserved.
Bayesian structural inference for hidden processes
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.
Adversarial life testing: A Bayesian negotiation model
International Nuclear Information System (INIS)
Rufo, M.J.; Martín, J.; Pérez, C.J.
2014-01-01
Life testing is a procedure intended for facilitating the process of making decisions in the context of industrial reliability. On the other hand, negotiation is a process of making joint decisions that has one of its main foundations in decision theory. A Bayesian sequential model of negotiation in the context of adversarial life testing is proposed. This model considers a general setting for which a manufacturer offers a product batch to a consumer. It is assumed that the reliability of the product is measured in terms of its lifetime. Furthermore, both the manufacturer and the consumer have to use their own information with respect to the quality of the product. Under these assumptions, two situations can be analyzed. For both of them, the main aim is to accept or reject the product batch based on the product reliability. This topic is related to a reliability demonstration problem. The procedure is applied to a class of distributions that belong to the exponential family. Thus, a unified framework addressing the main topics in the considered Bayesian model is presented. An illustrative example shows that the proposed technique can be easily applied in practice
Bayesian data analysis tools for atomic physics
Trassinelli, Martino
2017-10-01
We present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes' theorem and its applications. In particular we discuss about how to calculate simple and joint probability distributions and the Bayesian evidence, a model dependent quantity that allows to assign probabilities to different hypotheses from the analysis of a same data set. To give some practical examples, these methods are applied to two concrete cases. In the first example, the presence or not of a satellite line in an atomic spectrum is investigated. In the second example, we determine the most probable model among a set of possible profiles from the analysis of a statistically poor spectrum. We show also how to calculate the probability distribution of the main spectral component without having to determine uniquely the spectrum modeling. For these two studies, we implement the program Nested_fit to calculate the different probability distributions and other related quantities. Nested_fit is a Fortran90/Python code developed during the last years for analysis of atomic spectra. As indicated by the name, it is based on the nested algorithm, which is presented in details together with the program itself.
Distributed Bayesian Networks for User Modeling
DEFF Research Database (Denmark)
Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang
2006-01-01
The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used by such ada......The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used...... by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...
Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks
Directory of Open Access Journals (Sweden)
Fayroz F. Sherif
2015-01-01
Full Text Available Single nucleotide polymorphisms (SNPs contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer’s disease (AD. Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA studies. New SNP biomarkers were observed to be significantly associated with Alzheimer’s disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively.
Probabilistic Space Weather Forecasting: a Bayesian Perspective
Camporeale, E.; Chandorkar, M.; Borovsky, J.; Care', A.
2017-12-01
Most of the Space Weather forecasts, both at operational and research level, are not probabilistic in nature. Unfortunately, a prediction that does not provide a confidence level is not very useful in a decision-making scenario. Nowadays, forecast models range from purely data-driven, machine learning algorithms, to physics-based approximation of first-principle equations (and everything that sits in between). Uncertainties pervade all such models, at every level: from the raw data to finite-precision implementation of numerical methods. The most rigorous way of quantifying the propagation of uncertainties is by embracing a Bayesian probabilistic approach. One of the simplest and most robust machine learning technique in the Bayesian framework is Gaussian Process regression and classification. Here, we present the application of Gaussian Processes to the problems of the DST geomagnetic index forecast, the solar wind type classification, and the estimation of diffusion parameters in radiation belt modeling. In each of these very diverse problems, the GP approach rigorously provide forecasts in the form of predictive distributions. In turn, these distributions can be used as input for ensemble simulations in order to quantify the amplification of uncertainties. We show that we have achieved excellent results in all of the standard metrics to evaluate our models, with very modest computational cost.
Bayesian nonparametric adaptive control using Gaussian processes.
Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A
2015-03-01
Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.