Centaurea inexpugnabilis, a new species from section Acrocentron for the Iberian flora
Directory of Open Access Journals (Sweden)
P. P. Ferrer-Gallego
2017-01-01
Full Text Available A new species for the genus Centaurea L. (sect. Acrocentron, Asteraceae is described: C. inexpugnabilis, sp. nov. This plant has been found in the Mallos de Riglos (Riglos, Huesca, Spain, and its distribution is currently limited to the northeastern of the Iberian Peninsula. A description and diagnosis, iconography and several tables showing the main diagnostic characters to differentiate it from its related species is reported.
Ecological Panel Inference from Repeated Cross Sections
Pelzer, Ben; Eisinga, Rob; Franses, Philip Hans
2004-01-01
This chapter presents a Markov chain model for the estimation of individual-level binary transitions from a time series of independent repeated cross-sectional (RCS) samples. Although RCS samples lack direct information on individual turnover, it is demonstrated here that it is possible with these
DEFF Research Database (Denmark)
Møller, Jesper
(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.1 with the ......(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.......1 with the title ‘Inference'.) This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus...
DEFF Research Database (Denmark)
Møller, Jesper
2010-01-01
Chapter 9: This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo...... (MCMC) techniques. Due to space limitations the focus is on spatial point processes....
DEFF Research Database (Denmark)
Geiser, D.M.; Frisvad, Jens Christian; Taylor, J.W.
1998-01-01
are heterothallic. Phylogenetic relationships were inferred among members of Aspergillus section Fumigati based on partial DNA sequences from the benA beta-tubulin and rodA hydrophobin genes. Aspergillus clavatus was chosen as an outgroup. The two gene regions provided nearly equal numbers of phylogenetically...... informative nucleotide characters. The rodA region possessed a considerably higher level of inferred amino acid variation than did the benA region. The results of a partition homogeneity test showed that the benA and rodA data sets were not in significant conflict, and the topologies of the most parsimonious...
Staggemeier, Vanessa Graziele; Diniz-Filho, José Alexandre Felizola; Forest, Félix; Lucas, Eve
2015-04-01
Myrcia section Aulomyrcia includes ∼120 species that are endemic to the Neotropics and disjunctly distributed in the moist Amazon and Atlantic coastal forests of Brazil. This paper presents the first comprehensive phylogenetic study of this group and this phylogeny is used as a basis to evaluate recent classification systems and to test alternative hypotheses associated with the history of this clade. Fifty-three taxa were sampled out of the 120 species currently recognized, plus 40 outgroup taxa, for one nuclear marker (ribosomal internal transcribed spacer) and four plastid markers (psbA-trnH, trnL-trnF, trnQ-rpS16 and ndhF). The relationships were reconstructed based on Bayesian and maximum likelihood analyses. Additionally, a likelihood approach, 'geographic state speciation and extinction', was used to estimate region- dependent rates of speciation, extinction and dispersal, comparing historically climatic stable areas (refugia) and unstable areas. Maximum likelihood and Bayesian inferences indicate that Myrcia and Marlierea are polyphyletic, and the internal groupings recovered are characterized by combinations of morphological characters. Phylogenetic relationships support a link between Amazonian and north-eastern species and between north-eastern and south-eastern species. Lower extinction rates within glacial refugia suggest that these areas were important in maintaining diversity in the Atlantic forest biodiversity hotspot. This study provides a robust phylogenetic framework to address important ecological questions for Myrcia s.l. within an evolutionary context, and supports the need to unite taxonomically the two traditional genera Myrcia and Marlierea in an expanded Myrcia s.l. Furthermore, this study offers valuable insights into the diversification of plant species in the highly impacted Atlantic forest of South America; evidence is presented that the lowest extinction rates are found inside refugia and that range expansion from unstable areas
Repulsive wall potentials for He-(Ne,Ar,Kr,Xe) as inferred from differential cross sections
International Nuclear Information System (INIS)
Nitz, D.E.; Larson, J.; Swanson, K.; Wildgoose, C.
1993-01-01
We have obtained representations of the He-(Ne,Ar,Kr,Xe) ground state repulsive walls using model potentials optimized to fit published absolute differential cross section data for 500 and 1500 eV collisions. The HeNe potential, like the He 2 potentials can be fit using the semi-empirical HFD-B form constrained to cross zero at the proper internuclear separation. For the heavier atoms, this approach is unsuccessful and superior fits are obtained using only the purely repulsive V(r) - A EXP [-αr - βr 2 ] term of the HFD-B potential. Comparison of fitted potentials with previous results will be presented
International Nuclear Information System (INIS)
Hansen, L.F.; Grimes, S.M.; Pohl, B.A.; Poppe, C.H.; Wong, C.
1980-01-01
Differential cross sections for the (p,n) reactions to the isobaric analog states (IAS) of 232 Th and 238 U targets were measured at 26 and 27 MeV. The analysis of the data was done in conjunction with the proton elastic and inelastic (2 + , 4 + , 6 + ) differential cross sections measured at 26 MeV. Because collective effects are important in this mass region, deformed coupled-channels calculations were carried out for the simultaneous analysis of the proton and neutron outgoing channels. The sensitivity of the calculations was studied with respect to the optical model parameters used in the calculations, the shape of the nuclear charge distribution, the type of coupling scheme assumed among the levels, the magnitude of the deformation parameters, and the magnitude of the isovector potentials, V 1 and W 1 . A Lane model-consistent analysis of the data was used to infer optical potential parameters for 6- to 7-MeV neutrons. The neutron elastic differential cross sections obtained from these calculations are compared with measurements available in the literature, and with results obtained using neutron parameters from global sets reported at these energies. 7 figures, 3 tables
Lienkaemper, James J.; McFarland, Forrest S.; Simpson, Robert W.; Caskey, S. John
2014-01-01
Surface creep rate, observed along five branches of the dextral San Andreas fault system in northern California, varies considerably from one section to the next, indicating that so too may the depth at which the faults are locked. We model locking on 29 fault sections using each section’s mean long‐term creep rate and the consensus values of fault width and geologic slip rate. Surface creep rate observations from 111 short‐range alignment and trilateration arrays and 48 near‐fault, Global Positioning System station pairs are used to estimate depth of creep, assuming an elastic half‐space model and adjusting depth of creep iteratively by trial and error to match the creep observations along fault sections. Fault sections are delineated either by geometric discontinuities between them or by distinctly different creeping behaviors. We remove transient rate changes associated with five large (M≥5.5) regional earthquakes. Estimates of fraction locked, the ratio of moment accumulation rate to loading rate, on each section of the fault system provide a uniform means to inform source parameters relevant to seismic‐hazard assessment. From its mean creep rates, we infer the main branch (the San Andreas fault) ranges from only 20%±10% locked on its central creeping section to 99%–100% on the north coast. From mean accumulation rates, we infer that four urban faults appear to have accumulated enough seismic moment to produce major earthquakes: the northern Calaveras (M 6.8), Hayward (M 6.8), Rodgers Creek (M 7.1), and Green Valley (M 7.1). The latter three faults are nearing or past their mean recurrence interval.
Directory of Open Access Journals (Sweden)
M. V. Uspensky
2011-06-01
Full Text Available Norway and Finland STARE radar measurements in the eastward auroral electrojet are combined with EISCAT CP-1 measurements of the electron density and electric field vector in the common scattering volume to investigate the variation of the auroral radar volume cross section (VCS with the flow angle of observations (radar look direction with respect to the E×B electron drift. The data set available consists of ~6000 points for flow angles of 40–85° and electron drifts between 500 and 2000 m s−1. The EISCAT electron density N(h-profile data are used to estimate the effective electron density, aspect angle and thickness of the backscattering layer. It is shown that the flow angle variation of the VCS is rather weak, only ~5 dB within the range of the considered flow angles. The VCS values themselves respond almost linearly to the square of both the electron drift velocity magnitude and the effective electron density. By adopting the inferred shape of the VCS variation with the flow angle and the VCS dependence upon wavelength, the relative amplitude of electrostatic electron density fluctuations over all scales is estimated. Inferred values of 2–4 percent react nearly linearly to the electron drift velocity in the range of 500–1000 m s−1 but the rate of increase slows down at electron drifts >1000 m s−1 and density fluctuations of ~5.5 percent due to, perhaps, progressively growing nonlinear wave losses.
Energy Technology Data Exchange (ETDEWEB)
Youinou, Gilles Jean-Michel [Idaho National Lab. (INL), Idaho Falls, ID (United States)
2015-10-01
neutron irradiation allows to infer energy-integrated neutron cross sections, i.e. ∫₀^{∞}σ(E)φ(E)dE, where φ(E) is the neutron flux “seen” by the sample. This approach, which is usually defined and led by reactor physicists, is referred to as integral and is the object of this report. These two sources of information, i.e. differential and integral, are complementary and are used by the nuclear physicists in charge of producing the evaluated nuclear data files used by the nuclear community (ENDF, JEFF…). The generation of accurate nuclear data files requires an iterative process involving reactor physicists and nuclear data evaluators. This experimental program has been funded by the ATR National Scientific User Facility (ATR-NSUF) and by the DOE Office of Science in the framework of the Recovery Act. It has been given the name MANTRA for Measurement of Actinides Neutron TRAnsmutation.
Caticha, Ariel
2011-03-01
In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.
Kroese, A.H.; van der Meulen, E.A.; Poortema, Klaas; Schaafsma, W.
1995-01-01
The making of statistical inferences in distributional form is conceptionally complicated because the epistemic 'probabilities' assigned are mixtures of fact and fiction. In this respect they are essentially different from 'physical' or 'frequency-theoretic' probabilities. The distributional form is
Caticha, Ariel
2010-01-01
In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEn...
Aggelopoulos, Nikolaos C
2015-08-01
Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience. Copyright © 2015 Elsevier Ltd. All rights reserved.
Rohatgi, Vijay K
2003-01-01
Unified treatment of probability and statistics examines and analyzes the relationship between the two fields, exploring inferential issues. Numerous problems, examples, and diagrams--some with solutions--plus clear-cut, highlighted summaries of results. Advanced undergraduate to graduate level. Contents: 1. Introduction. 2. Probability Model. 3. Probability Distributions. 4. Introduction to Statistical Inference. 5. More on Mathematical Expectation. 6. Some Discrete Models. 7. Some Continuous Models. 8. Functions of Random Variables and Random Vectors. 9. Large-Sample Theory. 10. General Meth
Wang, Xiujuan; Lee, Myung W.; Collett, Timothy S.; Yang, Shengxiong; Guo, Yiqun; Wu, Shiguo
2014-01-01
Downhole wireline log (DWL) data was acquired from eight drill sites during China's first gas hydrate drilling expedition (GMGS-1) in 2007. Initial analyses of the acquired well log data suggested that there were no significant gas hydrate occurrences at Site SH4. However, the re-examination of the DWL data from Site SH4 indicated that there are two intervals of high resistivity, which could be indicative of gas hydrate. One interval of high resistivity at depth of 171–175 m below seafloor (mbsf) is associated with a high compressional- wave (P-wave) velocities and low gamma ray log values, which suggests the presence of gas hydrate in a potentially sand-rich (low clay content) sedimentary section. The second high resistivity interval at depth of 175–180 mbsf is associated with low P-wave velocities and low gamma values, which suggests the presence of free gas in a potentially sand-rich (low clay content) sedimentary section. Because the occurrence of free gas is much shallower than the expected from the regional depth of the bottom simulating reflector (BSR), the free gas could be from the dissociation of gas hydrate during drilling or there may be a local anomaly in the depth to the base of the gas hydrate stability zone. In order to determine whether the low P-wave velocity with high resistivity is caused by in-situ free gas or dissociated free gas from the gas hydrate, the surface seismic data were also used in this analysis. The log analysis incorporating the surface seismic data through the construction of synthetic seismograms using various models indicated the presence of free gas directly in contact with an overlying gas hydrate-bearing section. The occurrence of the anomalous base of gas hydrate stability at Site SH4 could be caused by a local heat flow conditions. This paper documents the first observation of gas hydrate in what is believed to be a sand-rich sediment in Shenhu area of the South China Sea.
DEFF Research Database (Denmark)
Andersen, Jesper
2009-01-01
Collateral evolution the problem of updating several library-using programs in response to API changes in the used library. In this dissertation we address the issue of understanding collateral evolutions by automatically inferring a high-level specification of the changes evident in a given set ...... specifications inferred by spdiff in Linux are shown. We find that the inferred specifications concisely capture the actual collateral evolution performed in the examples....
Energy Technology Data Exchange (ETDEWEB)
Petrov, S.
1996-10-01
Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.
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.
Geometric statistical inference
International Nuclear Information System (INIS)
Periwal, Vipul
1999-01-01
A reparametrization-covariant formulation of the inverse problem of probability is explicitly solved for finite sample sizes. The inferred distribution is explicitly continuous for finite sample size. A geometric solution of the statistical inference problem in higher dimensions is outlined
Bailer-Jones, Coryn A. L.
2017-04-01
Preface; 1. Probability basics; 2. Estimation and uncertainty; 3. Statistical models and inference; 4. Linear models, least squares, and maximum likelihood; 5. Parameter estimation: single parameter; 6. Parameter estimation: multiple parameters; 7. Approximating distributions; 8. Monte Carlo methods for inference; 9. Parameter estimation: Markov chain Monte Carlo; 10. Frequentist hypothesis testing; 11. Model comparison; 12. Dealing with more complicated problems; References; Index.
Nagao, Makoto
1990-01-01
Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of """"knowledge"""" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intellig
Logical inference and evaluation
International Nuclear Information System (INIS)
Perey, F.G.
1981-01-01
Most methodologies of evaluation currently used are based upon the theory of statistical inference. It is generally perceived that this theory is not capable of dealing satisfactorily with what are called systematic errors. Theories of logical inference should be capable of treating all of the information available, including that not involving frequency data. A theory of logical inference is presented as an extension of deductive logic via the concept of plausibility and the application of group theory. Some conclusions, based upon the application of this theory to evaluation of data, are also given
Probability and Statistical Inference
Prosper, Harrison B.
2006-01-01
These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.
On quantum statistical inference
Barndorff-Nielsen, O.E.; Gill, R.D.; Jupp, P.E.
2003-01-01
Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, developments in the theory of quantum measurements have
2018-02-15
expressed a variety of inference techniques on discrete and continuous distributions: exact inference, importance sampling, Metropolis-Hastings (MH...without redoing any math or rewriting any code. And although our main goal is composable reuse, our performance is also good because we can use...control paths. • The Hakaru language can express mixtures of discrete and continuous distributions, but the current disintegration transformation
Introductory statistical inference
Mukhopadhyay, Nitis
2014-01-01
This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques.Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of dist
Type Inference with Inequalities
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff
1991-01-01
of (monotonic) inequalities on the types of variables and expressions. A general result about systems of inequalities over semilattices yields a solvable form. We distinguish between deciding typability (the existence of solutions) and type inference (the computation of a minimal solution). In our case, both......Type inference can be phrased as constraint-solving over types. We consider an implicitly typed language equipped with recursive types, multiple inheritance, 1st order parametric polymorphism, and assignments. Type correctness is expressed as satisfiability of a possibly infinite collection...
Watson, Jane
2007-01-01
Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…
Hybrid Optical Inference Machines
1991-09-27
with labels. Now, events. a set of facts cal be generated in the dyadic form "u, R 1,2" Eichmann and Caulfield (19] consider the same type of and can...these enceding-schemes. These architectures are-based pri- 19. G. Eichmann and H. J. Caulfield, "Optical Learning (Inference)marily on optical inner
Inference rule and problem solving
Energy Technology Data Exchange (ETDEWEB)
Goto, S
1982-04-01
Intelligent information processing signifies an opportunity of having man's intellectual activity executed on the computer, in which inference, in place of ordinary calculation, is used as the basic operational mechanism for such an information processing. Many inference rules are derived from syllogisms in formal logic. The problem of programming this inference function is referred to as a problem solving. Although logically inference and problem-solving are in close relation, the calculation ability of current computers is on a low level for inferring. For clarifying the relation between inference and computers, nonmonotonic logic has been considered. The paper deals with the above topics. 16 references.
Stochastic processes inference theory
Rao, Malempati M
2014-01-01
This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.
Making Type Inference Practical
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff; Oxhøj, Nicholas; Palsberg, Jens
1992-01-01
We present the implementation of a type inference algorithm for untyped object-oriented programs with inheritance, assignments, and late binding. The algorithm significantly improves our previous one, presented at OOPSLA'91, since it can handle collection classes, such as List, in a useful way. Abo......, the complexity has been dramatically improved, from exponential time to low polynomial time. The implementation uses the techniques of incremental graph construction and constraint template instantiation to avoid representing intermediate results, doing superfluous work, and recomputing type information....... Experiments indicate that the implementation type checks as much as 100 lines pr. second. This results in a mature product, on which a number of tools can be based, for example a safety tool, an image compression tool, a code optimization tool, and an annotation tool. This may make type inference for object...
Directory of Open Access Journals (Sweden)
João Paulo Monteiro
2001-12-01
Full Text Available Russell's The Problems of Philosophy tries to establish a new theory of induction, at the same time that Hume is there accused of an irrational/ scepticism about induction". But a careful analysis of the theory of knowledge explicitly acknowledged by Hume reveals that, contrary to the standard interpretation in the XXth century, possibly influenced by Russell, Hume deals exclusively with causal inference (which he never classifies as "causal induction", although now we are entitled to do so, never with inductive inference in general, mainly generalizations about sensible qualities of objects ( whether, e.g., "all crows are black" or not is not among Hume's concerns. Russell's theories are thus only false alternatives to Hume's, in (1912 or in his (1948.
Causal inference in econometrics
Kreinovich, Vladik; Sriboonchitta, Songsak
2016-01-01
This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
Active inference and learning.
Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni
2016-09-01
This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Learning Convex Inference of Marginals
Domke, Justin
2012-01-01
Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suffers difficulties when either the inference process or the model is approximate. In this paper, the inference process is first defined to be the minimization of a convex function, inspired by free energy approximations. Learning is then done directly in terms of the performance of the inference process at univariate marginal prediction. The main ...
Probabilistic inductive inference: a survey
Ambainis, Andris
2001-01-01
Inductive inference is a recursion-theoretic theory of learning, first developed by E. M. Gold (1967). This paper surveys developments in probabilistic inductive inference. We mainly focus on finite inference of recursive functions, since this simple paradigm has produced the most interesting (and most complex) results.
Multimodel inference and adaptive management
Rehme, S.E.; Powell, L.A.; Allen, Craig R.
2011-01-01
Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
Emotional inferences by pragmatics
Iza-Miqueleiz, Mauricio
2017-01-01
It has for long been taken for granted that, along the course of reading a text, world knowledge is often required in order to establish coherent links between sentences (McKoon & Ratcliff 1992, Iza & Ezquerro 2000). The content grasped from a text turns out to be strongly dependent upon the reader’s additional knowledge that allows a coherent interpretation of the text as a whole. The world knowledge directing the inference may be of distinctive nature. Gygax et al. (2007) showed that m...
DEFF Research Database (Denmark)
Andersen, Jesper; Lawall, Julia
2010-01-01
A key issue in maintaining Linux device drivers is the need to keep them up to date with respect to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spdiff, that identifies common changes...... developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect...
Feature Inference Learning and Eyetracking
Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.
2009-01-01
Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…
An Inference Language for Imaging
DEFF Research Database (Denmark)
Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen
2014-01-01
We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framewor...
Energy Technology Data Exchange (ETDEWEB)
Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Ahn, Sungsoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of); Shin, Jinwoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of)
2017-05-25
Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM). Since it is computationally intractable, approximate methods have been used to resolve the issue in practice, where meanfield (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments, on complete GMs of relatively small size and on large GM (up-to 300 variables) confirm that the newly proposed algorithms outperform and generalize MF and BP.
Social Inference Through Technology
Oulasvirta, Antti
Awareness cues are computer-mediated, real-time indicators of people’s undertakings, whereabouts, and intentions. Already in the mid-1970 s, UNIX users could use commands such as “finger” and “talk” to find out who was online and to chat. The small icons in instant messaging (IM) applications that indicate coconversants’ presence in the discussion space are the successors of “finger” output. Similar indicators can be found in online communities, media-sharing services, Internet relay chat (IRC), and location-based messaging applications. But presence and availability indicators are only the tip of the iceberg. Technological progress has enabled richer, more accurate, and more intimate indicators. For example, there are mobile services that allow friends to query and follow each other’s locations. Remote monitoring systems developed for health care allow relatives and doctors to assess the wellbeing of homebound patients (see, e.g., Tang and Venables 2000). But users also utilize cues that have not been deliberately designed for this purpose. For example, online gamers pay attention to other characters’ behavior to infer what the other players are like “in real life.” There is a common denominator underlying these examples: shared activities rely on the technology’s representation of the remote person. The other human being is not physically present but present only through a narrow technological channel.
Optimization methods for logical inference
Chandru, Vijay
2011-01-01
Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though ""solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs."" Presenting powerful, proven optimization techniques for logic in
On principles of inductive inference
Kostecki, Ryszard Paweł
2011-01-01
We propose an intersubjective epistemic approach to foundations of probability theory and statistical inference, based on relative entropy and category theory, and aimed to bypass the mathematical and conceptual problems of existing foundational approaches.
Statistical inference via fiducial methods
Salomé, Diemer
1998-01-01
In this thesis the attention is restricted to inductive reasoning using a mathematical probability model. A statistical procedure prescribes, for every theoretically possible set of data, the inference about the unknown of interest. ... Zie: Summary
Statistical inference for stochastic processes
National Research Council Canada - National Science Library
Basawa, Ishwar V; Prakasa Rao, B. L. S
1980-01-01
The aim of this monograph is to attempt to reduce the gap between theory and applications in the area of stochastic modelling, by directing the interest of future researchers to the inference aspects...
Active inference, communication and hermeneutics.
Friston, Karl J; Frith, Christopher D
2015-07-01
Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Optimal inference with suboptimal models: Addiction and active Bayesian inference
Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl
2015-01-01
When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321
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....
On Maximum Entropy and Inference
Directory of Open Access Journals (Sweden)
Luigi Gresele
2017-11-01
Full Text Available Maximum entropy is a powerful concept that entails a sharp separation between relevant and irrelevant variables. It is typically invoked in inference, once an assumption is made on what the relevant variables are, in order to estimate a model from data, that affords predictions on all other (dependent variables. Conversely, maximum entropy can be invoked to retrieve the relevant variables (sufficient statistics directly from the data, once a model is identified by Bayesian model selection. We explore this approach in the case of spin models with interactions of arbitrary order, and we discuss how relevant interactions can be inferred. In this perspective, the dimensionality of the inference problem is not set by the number of parameters in the model, but by the frequency distribution of the data. We illustrate the method showing its ability to recover the correct model in a few prototype cases and discuss its application on a real dataset.
Eight challenges in phylodynamic inference
Directory of Open Access Journals (Sweden)
Simon D.W. Frost
2015-03-01
Full Text Available The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.
Problem solving and inference mechanisms
Energy Technology Data Exchange (ETDEWEB)
Furukawa, K; Nakajima, R; Yonezawa, A; Goto, S; Aoyama, A
1982-01-01
The heart of the fifth generation computer will be powerful mechanisms for problem solving and inference. A deduction-oriented language is to be designed, which will form the core of the whole computing system. The language is based on predicate logic with the extended features of structuring facilities, meta structures and relational data base interfaces. Parallel computation mechanisms and specialized hardware architectures are being investigated to make possible efficient realization of the language features. The project includes research into an intelligent programming system, a knowledge representation language and system, and a meta inference system to be built on the core. 30 references.
Object-Oriented Type Inference
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff; Palsberg, Jens
1991-01-01
We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an op...
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....
Mixed normal inference on multicointegration
Boswijk, H.P.
2009-01-01
Asymptotic likelihood analysis of cointegration in I(2) models, see Johansen (1997, 2006), Boswijk (2000) and Paruolo (2000), has shown that inference on most parameters is mixed normal, implying hypothesis test statistics with an asymptotic 2 null distribution. The asymptotic distribution of the
Statistical inference and Aristotle's Rhetoric.
Macdonald, Ranald R
2004-11-01
Formal logic operates in a closed system where all the information relevant to any conclusion is present, whereas this is not the case when one reasons about events and states of the world. Pollard and Richardson drew attention to the fact that the reasoning behind statistical tests does not lead to logically justifiable conclusions. In this paper statistical inferences are defended not by logic but by the standards of everyday reasoning. Aristotle invented formal logic, but argued that people mostly get at the truth with the aid of enthymemes--incomplete syllogisms which include arguing from examples, analogies and signs. It is proposed that statistical tests work in the same way--in that they are based on examples, invoke the analogy of a model and use the size of the effect under test as a sign that the chance hypothesis is unlikely. Of existing theories of statistical inference only a weak version of Fisher's takes this into account. Aristotle anticipated Fisher by producing an argument of the form that there were too many cases in which an outcome went in a particular direction for that direction to be plausibly attributed to chance. We can therefore conclude that Aristotle would have approved of statistical inference and there is a good reason for calling this form of statistical inference classical.
Statistical learning and selective inference.
Taylor, Jonathan; Tibshirani, Robert J
2015-06-23
We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.
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...
Statistical inference an integrated approach
Migon, Helio S; Louzada, Francisco
2014-01-01
Introduction Information The concept of probability Assessing subjective probabilities An example Linear algebra and probability Notation Outline of the bookElements of Inference Common statistical modelsLikelihood-based functions Bayes theorem Exchangeability Sufficiency and exponential family Parameter elimination Prior Distribution Entirely subjective specification Specification through functional forms Conjugacy with the exponential family Non-informative priors Hierarchical priors Estimation Introduction to decision theoryBayesian point estimation Classical point estimation Empirical Bayes estimation Comparison of estimators Interval estimation Estimation in the Normal model Approximating Methods The general problem of inference Optimization techniquesAsymptotic theory Other analytical approximations Numerical integration methods Simulation methods Hypothesis Testing Introduction Classical hypothesis testingBayesian hypothesis testing Hypothesis testing and confidence intervalsAsymptotic tests Prediction...
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.
Causal inference based on counterfactuals
Directory of Open Access Journals (Sweden)
Höfler M
2005-09-01
Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.
System Support for Forensic Inference
Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan
Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.
Probability biases as Bayesian inference
Directory of Open Access Journals (Sweden)
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.
Statistical inference on residual life
Jeong, Jong-Hyeon
2014-01-01
This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function, especially for communications between patients and physicians regarding the efficacy of a new drug in the medical field. This book reviews existing statistical methods to infer the residual life distribution. The review and comparison includes existing inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept of the residual life is also extended to competing risks analysis. The targeted audience includes biostatisticians, graduate students, and PhD (bio)statisticians. Knowledge in survival analysis at an introductory graduate level is advisable prior to reading this book.
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...
Bayesian inference data evaluation and decisions
Harney, Hanns Ludwig
2016-01-01
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This is particularly useful when the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins, so that the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. New sections feature factorizing parameters, commuting parameters, observables in quantum mechanics, the art of fitting with coherent and with incoherent alternatives and fitting with multinomial distribution. Additional problems and examples help deepen the knowledge. Requiring no knowledge of quantum mechanics, the book is written on introductory level, with man...
Progression inference for somatic mutations in cancer
Directory of Open Access Journals (Sweden)
Leif E. Peterson
2017-04-01
Full Text Available Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mutations, deletions, amplifications, downregulation, and upregulation among the top 20 driver genes associated with each cancer. Results indicate that the majority of hierarchies involved TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, VHL, etc. Research into the order and accumulation of genomic alterations among cancer driver genes will ever-increase as the costs of nextgen sequencing subside, and personalized/precision medicine incorporates whole-genome scans into the diagnosis and treatment of cancer. Keywords: Oncology, Cancer research, Genetics, Computational biology
Statistical inference a short course
Panik, Michael J
2012-01-01
A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causal
On Quantum Statistical Inference, II
Barndorff-Nielsen, O. E.; Gill, R. D.; Jupp, P. E.
2003-01-01
Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, theoretical developments in the theory of quantum measurements have brought the basic mathematical framework for the probability calculations much closer to that of classical probability theory. The present paper reviews this field and proposes and inte...
Nonparametric predictive inference in reliability
International Nuclear Information System (INIS)
Coolen, F.P.A.; Coolen-Schrijner, P.; Yan, K.J.
2002-01-01
We introduce a recently developed statistical approach, called nonparametric predictive inference (NPI), to reliability. Bounds for the survival function for a future observation are presented. We illustrate how NPI can deal with right-censored data, and discuss aspects of competing risks. We present possible applications of NPI for Bernoulli data, and we briefly outline applications of NPI for replacement decisions. The emphasis is on introduction and illustration of NPI in reliability contexts, detailed mathematical justifications are presented elsewhere
Variational inference & deep learning : A new synthesis
Kingma, D.P.
2017-01-01
In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.
Variational inference & deep learning: A new synthesis
Kingma, D.P.
2017-01-01
In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.
Continuous Integrated Invariant Inference, Phase I
National Aeronautics and Space Administration — The proposed project will develop a new technique for invariant inference and embed this and other current invariant inference and checking techniques in an...
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
Adaptive Inference on General Graphical Models
Acar, Umut A.; Ihler, Alexander T.; Mettu, Ramgopal; Sumer, Ozgur
2012-01-01
Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional ...
Sweller, Naomi; Hayes, Brett K
2010-08-01
Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.
Generative inference for cultural evolution.
Kandler, Anne; Powell, Adam
2018-04-05
One of the major challenges in cultural evolution is to understand why and how various forms of social learning are used in human populations, both now and in the past. To date, much of the theoretical work on social learning has been done in isolation of data, and consequently many insights focus on revealing the learning processes or the distributions of cultural variants that are expected to have evolved in human populations. In population genetics, recent methodological advances have allowed a greater understanding of the explicit demographic and/or selection mechanisms that underlie observed allele frequency distributions across the globe, and their change through time. In particular, generative frameworks-often using coalescent-based simulation coupled with approximate Bayesian computation (ABC)-have provided robust inferences on the human past, with no reliance on a priori assumptions of equilibrium. Here, we demonstrate the applicability and utility of generative inference approaches to the field of cultural evolution. The framework advocated here uses observed population-level frequency data directly to establish the likely presence or absence of particular hypothesized learning strategies. In this context, we discuss the problem of equifinality and argue that, in the light of sparse cultural data and the multiplicity of possible social learning processes, the exclusion of those processes inconsistent with the observed data might be the most instructive outcome. Finally, we summarize the findings of generative inference approaches applied to a number of case studies.This article is part of the theme issue 'Bridging cultural gaps: interdisciplinary studies in human cultural evolution'. © 2018 The Author(s).
sick: The Spectroscopic Inference Crank
Casey, Andrew R.
2016-03-01
There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal
Inferring network structure from cascades
Ghonge, Sushrut; Vural, Dervis Can
2017-07-01
Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.
SICK: THE SPECTROSCOPIC INFERENCE CRANK
Energy Technology Data Exchange (ETDEWEB)
Casey, Andrew R., E-mail: arc@ast.cam.ac.uk [Institute of Astronomy, University of Cambridge, Madingley Road, Cambdridge, CB3 0HA (United Kingdom)
2016-03-15
There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal
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....
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.
SICK: THE SPECTROSCOPIC INFERENCE CRANK
International Nuclear Information System (INIS)
Casey, Andrew R.
2016-01-01
There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal
Subjective randomness as statistical inference.
Griffiths, Thomas L; Daniels, Dylan; Austerweil, Joseph L; Tenenbaum, Joshua B
2018-06-01
Some events seem more random than others. For example, when tossing a coin, a sequence of eight heads in a row does not seem very random. Where do these intuitions about randomness come from? We argue that subjective randomness can be understood as the result of a statistical inference assessing the evidence that an event provides for having been produced by a random generating process. We show how this account provides a link to previous work relating randomness to algorithmic complexity, in which random events are those that cannot be described by short computer programs. Algorithmic complexity is both incomputable and too general to capture the regularities that people can recognize, but viewing randomness as statistical inference provides two paths to addressing these problems: considering regularities generated by simpler computing machines, and restricting the set of probability distributions that characterize regularity. Building on previous work exploring these different routes to a more restricted notion of randomness, we define strong quantitative models of human randomness judgments that apply not just to binary sequences - which have been the focus of much of the previous work on subjective randomness - but also to binary matrices and spatial clustering. Copyright © 2018 Elsevier Inc. All rights reserved.
Lower complexity bounds for lifted inference
DEFF Research Database (Denmark)
Jaeger, Manfred
2015-01-01
instances of the model. Numerous approaches for such “lifted inference” techniques have been proposed. While it has been demonstrated that these techniques will lead to significantly more efficient inference on some specific models, there are only very recent and still quite restricted results that show...... the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established earlier in Jaeger (2000; Jaeger, M. 2000. On the complexity of inference about...... that under the assumption that NETIME≠ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier-, and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference and for knowledge bases not containing...
Statistical inference for financial engineering
Taniguchi, Masanobu; Ogata, Hiroaki; Taniai, Hiroyuki
2014-01-01
This monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered as well. This book also includes several statistical approaches, e.g., discriminant analysis, the empirical likelihood method, control variate method, quantile regression, realized volatility etc., which have been recently developed and are considered to be powerful tools for analyzing the financial data, establishing a new bridge between time series and financial engineering. This book is well suited as a professional reference book on finance, statistics and statistical financial engineering. Readers are expected to have an undergraduate-level knowledge of statistics.
Type inference for correspondence types
DEFF Research Database (Denmark)
Hüttel, Hans; Gordon, Andy; Hansen, Rene Rydhof
2009-01-01
We present a correspondence type/effect system for authenticity in a π-calculus with polarized channels, dependent pair types and effect terms and show how one may, given a process P and an a priori type environment E, generate constraints that are formulae in the Alternating Least Fixed......-Point (ALFP) logic. We then show how a reasonable model of the generated constraints yields a type/effect assignment such that P becomes well-typed with respect to E if and only if this is possible. The formulae generated satisfy a finite model property; a system of constraints is satisfiable if and only...... if it has a finite model. As a consequence, we obtain the result that type/effect inference in our system is polynomial-time decidable....
Causal inference in public health.
Glass, Thomas A; Goodman, Steven N; Hernán, Miguel A; Samet, Jonathan M
2013-01-01
Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action's consequences rather than the less precise notion of a risk factor's causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world.
Inference Attacks and Control on Database Structures
Directory of Open Access Journals (Sweden)
Muhamed Turkanovic
2015-02-01
Full Text Available Today’s databases store information with sensitivity levels that range from public to highly sensitive, hence ensuring confidentiality can be highly important, but also requires costly control. This paper focuses on the inference problem on different database structures. It presents possible treats on privacy with relation to the inference, and control methods for mitigating these treats. The paper shows that using only access control, without any inference control is inadequate, since these models are unable to protect against indirect data access. Furthermore, it covers new inference problems which rise from the dimensions of new technologies like XML, semantics, etc.
... birth after a C-section, called a VBAC ) Emergency C-Sections Some C-sections are unexpected emergency ... side to nurse or using the clutch (or football) hold can take the pressure off your abdomen. ...
LAIT: a local ancestry inference toolkit.
Hui, Daniel; Fang, Zhou; Lin, Jerome; Duan, Qing; Li, Yun; Hu, Ming; Chen, Wei
2017-09-06
Inferring local ancestry in individuals of mixed ancestry has many applications, most notably in identifying disease-susceptible loci that vary among different ethnic groups. Many software packages are available for inferring local ancestry in admixed individuals. However, most of these existing software packages require specific formatted input files and generate output files in various types, yielding practical inconvenience. We developed a tool set, Local Ancestry Inference Toolkit (LAIT), which can convert standardized files into software-specific input file formats as well as standardize and summarize inference results for four popular local ancestry inference software: HAPMIX, LAMP, LAMP-LD, and ELAI. We tested LAIT using both simulated and real data sets and demonstrated that LAIT provides convenience to run multiple local ancestry inference software. In addition, we evaluated the performance of local ancestry software among different supported software packages, mainly focusing on inference accuracy and computational resources used. We provided a toolkit to facilitate the use of local ancestry inference software, especially for users with limited bioinformatics background.
Forward and backward inference in spatial cognition.
Directory of Open Access Journals (Sweden)
Will D Penny
Full Text Available This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.
Generative Inferences Based on Learned Relations
Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.
2017-01-01
A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…
Inference in models with adaptive learning
Chevillon, G.; Massmann, M.; Mavroeidis, S.
2010-01-01
Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be
Fiducial inference - A Neyman-Pearson interpretation
Salome, D; VonderLinden, W; Dose,; Fischer, R; Preuss, R
1999-01-01
Fisher's fiducial argument is a tool for deriving inferences in the form of a probability distribution on the parameter space, not based on Bayes's Theorem. Lindley established that in exceptional situations fiducial inferences coincide with posterior distributions; in the other situations fiducial
Uncertainty in prediction and in inference
Hilgevoord, J.; Uffink, J.
1991-01-01
The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close re-lationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in
Causal inference in economics and marketing.
Varian, Hal R
2016-07-05
This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.
Nonparametric predictive inference in statistical process control
Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.
2000-01-01
New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on
The Impact of Disablers on Predictive Inference
Cummins, Denise Dellarosa
2014-01-01
People consider alternative causes when deciding whether a cause is responsible for an effect (diagnostic inference) but appear to neglect them when deciding whether an effect will occur (predictive inference). Five experiments were conducted to test a 2-part explanation of this phenomenon: namely, (a) that people interpret standard predictive…
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...
Extended likelihood inference in reliability
International Nuclear Information System (INIS)
Martz, H.F. Jr.; Beckman, R.J.; Waller, R.A.
1978-10-01
Extended likelihood methods of inference are developed in which subjective information in the form of a prior distribution is combined with sampling results by means of an extended likelihood function. The extended likelihood function is standardized for use in obtaining extended likelihood intervals. Extended likelihood intervals are derived for the mean of a normal distribution with known variance, the failure-rate of an exponential distribution, and the parameter of a binomial distribution. Extended second-order likelihood methods are developed and used to solve several prediction problems associated with the exponential and binomial distributions. In particular, such quantities as the next failure-time, the number of failures in a given time period, and the time required to observe a given number of failures are predicted for the exponential model with a gamma prior distribution on the failure-rate. In addition, six types of life testing experiments are considered. For the binomial model with a beta prior distribution on the probability of nonsurvival, methods are obtained for predicting the number of nonsurvivors in a given sample size and for predicting the required sample size for observing a specified number of nonsurvivors. Examples illustrate each of the methods developed. Finally, comparisons are made with Bayesian intervals in those cases where these are known to exist
Reinforcement learning or active inference?
Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J
2009-07-29
This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.
Reinforcement learning or active inference?
Directory of Open Access Journals (Sweden)
Karl J Friston
2009-07-01
Full Text Available This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.
Active inference and epistemic value.
Friston, Karl; Rigoli, Francesco; Ognibene, Dimitri; Mathys, Christoph; Fitzgerald, Thomas; Pezzulo, Giovanni
2015-01-01
We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.
Ancient Biomolecules and Evolutionary Inference.
Cappellini, Enrico; Prohaska, Ana; Racimo, Fernando; Welker, Frido; Pedersen, Mikkel Winther; Allentoft, Morten E; de Barros Damgaard, Peter; Gutenbrunner, Petra; Dunne, Julie; Hammann, Simon; Roffet-Salque, Mélanie; Ilardo, Melissa; Moreno-Mayar, J Víctor; Wang, Yucheng; Sikora, Martin; Vinner, Lasse; Cox, Jürgen; Evershed, Richard P; Willerslev, Eske
2018-04-25
Over the last decade, studies of ancient biomolecules-particularly ancient DNA, proteins, and lipids-have revolutionized our understanding of evolutionary history. Though initially fraught with many challenges, the field now stands on firm foundations. Researchers now successfully retrieve nucleotide and amino acid sequences, as well as lipid signatures, from progressively older samples, originating from geographic areas and depositional environments that, until recently, were regarded as hostile to long-term preservation of biomolecules. Sampling frequencies and the spatial and temporal scope of studies have also increased markedly, and with them the size and quality of the data sets generated. This progress has been made possible by continuous technical innovations in analytical methods, enhanced criteria for the selection of ancient samples, integrated experimental methods, and advanced computational approaches. Here, we discuss the history and current state of ancient biomolecule research, its applications to evolutionary inference, and future directions for this young and exciting field. Expected final online publication date for the Annual Review of Biochemistry Volume 87 is June 20, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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...
EI: A Program for Ecological Inference
Directory of Open Access Journals (Sweden)
Gary King
2004-09-01
Full Text Available The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King 1997. Ecological inference, as traditionally defined, is the process of using aggregate (i.e., "ecological" data to infer discrete individual-level relationships of interest when individual-level data are not available. Ecological inferences are required in political science research when individual-level surveys are unavailable (e.g., local or comparative electoral politics, unreliable (racial politics, insufficient (political geography, or infeasible (political history. They are also required in numerous areas of ma jor significance in public policy (e.g., for applying the Voting Rights Act and other academic disciplines ranging from epidemiology and marketing to sociology and quantitative history.
Statistical inference an integrated Bayesianlikelihood approach
Aitkin, Murray
2010-01-01
Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It pre
Inferring Domain Plans in Question-Answering
National Research Council Canada - National Science Library
Pollack, Martha E
1986-01-01
The importance of plan inference in models of conversation has been widely noted in the computational-linguistics literature, and its incorporation in question-answering systems has enabled a range...
Scalable inference for stochastic block models
Peng, Chengbin; Zhang, Zhihua; Wong, Ka-Chun; Zhang, Xiangliang; Keyes, David E.
2017-01-01
Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of "big data," traditional inference
Efficient algorithms for conditional independence inference
Czech Academy of Sciences Publication Activity Database
Bouckaert, R.; Hemmecke, R.; Lindner, S.; Studený, Milan
2010-01-01
Roč. 11, č. 1 (2010), s. 3453-3479 ISSN 1532-4435 R&D Projects: GA ČR GA201/08/0539; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : conditional independence inference * linear programming approach Subject RIV: BA - General Mathematics Impact factor: 2.949, year: 2010 http://library.utia.cas.cz/separaty/2010/MTR/studeny-efficient algorithms for conditional independence inference.pdf
On the criticality of inferred models
Mastromatteo, Iacopo; Marsili, Matteo
2011-10-01
Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. This region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time scales naturally yield models which are close to criticality.
On the criticality of inferred models
International Nuclear Information System (INIS)
Mastromatteo, Iacopo; Marsili, Matteo
2011-01-01
Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. This region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time scales naturally yield models which are close to criticality
Polynomial Chaos Surrogates for Bayesian Inference
Le Maitre, Olivier
2016-01-06
The Bayesian inference is a popular probabilistic method to solve inverse problems, such as the identification of field parameter in a PDE model. The inference rely on the Bayes rule to update the prior density of the sought field, from observations, and derive its posterior distribution. In most cases the posterior distribution has no explicit form and has to be sampled, for instance using a Markov-Chain Monte Carlo method. In practice the prior field parameter is decomposed and truncated (e.g. by means of Karhunen- Lo´eve decomposition) to recast the inference problem into the inference of a finite number of coordinates. Although proved effective in many situations, the Bayesian inference as sketched above faces several difficulties requiring improvements. First, sampling the posterior can be a extremely costly task as it requires multiple resolutions of the PDE model for different values of the field parameter. Second, when the observations are not very much informative, the inferred parameter field can highly depends on its prior which can be somehow arbitrary. These issues have motivated the introduction of reduced modeling or surrogates for the (approximate) determination of the parametrized PDE solution and hyperparameters in the description of the prior field. Our contribution focuses on recent developments in these two directions: the acceleration of the posterior sampling by means of Polynomial Chaos expansions and the efficient treatment of parametrized covariance functions for the prior field. We also discuss the possibility of making such approach adaptive to further improve its efficiency.
Saddlepoint Approximations in Conditional Inference
1990-06-11
Then the inverse transform can be written as (%, Y) = (T, q(T, Z)) for some function q. When the transform is not one to one, the domain should be...general regularity conditions described at the beginning of this section hold and that the solution t1 in (9) exists. Denote the inverse transform by (X, Y...density hn(t 0 l z) are desired. Then the inverse transform (Y, ) = (T, q(T, Z)) exists and the variable v in the cumulant generating function K(u, v
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...
A formal model of interpersonal inference
Directory of Open Access Journals (Sweden)
Michael eMoutoussis
2014-03-01
Full Text Available Introduction: We propose that active Bayesian inference – a general framework for decision-making – can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance. Method: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study. The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference. Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others. The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory. Results: 1. Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred. This inference, akin to 'mentalising' in the psychological literature, is based upon the outcomes of interpersonal exchanges. 2. We show how some well-known social-psychological phenomena (e.g. self-serving biases can be explained in terms of active interpersonal inference. 3. Mentalising naturally entails Bayesian updating of how people value social outcomes. Crucially this includes inference about one’s own qualities and preferences. Conclusion: We inaugurate a Bayes optimal framework for modelling intersubject variability in mentalising during interpersonal exchanges. Here, interpersonal representations are endowed with explicit functional and affective properties. We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalising is distorted.
Estimating uncertainty of inference for validation
Energy Technology Data Exchange (ETDEWEB)
Booker, Jane M [Los Alamos National Laboratory; Langenbrunner, James R [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Ross, Timothy J [UNM
2010-09-30
We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the
Deep Learning for Population Genetic Inference.
Sheehan, Sara; Song, Yun S
2016-03-01
Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.
Deep Learning for Population Genetic Inference.
Directory of Open Access Journals (Sweden)
Sara Sheehan
2016-03-01
Full Text Available Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data to the output (e.g., population genetic parameters of interest. We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history. Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.
Deep Learning for Population Genetic Inference
Sheehan, Sara; Song, Yun S.
2016-01-01
Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908
Inferring Phylogenetic Networks Using PhyloNet.
Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay
2018-07-01
PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.
Goal inferences about robot behavior : goal inferences and human response behaviors
Broers, H.A.T.; Ham, J.R.C.; Broeders, R.; De Silva, P.; Okada, M.
2014-01-01
This explorative research focused on the goal inferences human observers draw based on a robot's behavior, and the extent to which those inferences predict people's behavior in response to that robot. Results show that different robot behaviors cause different response behavior from people.
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…
Explanatory Preferences Shape Learning and Inference.
Lombrozo, Tania
2016-10-01
Explanations play an important role in learning and inference. People often learn by seeking explanations, and they assess the viability of hypotheses by considering how well they explain the data. An emerging body of work reveals that both children and adults have strong and systematic intuitions about what constitutes a good explanation, and that these explanatory preferences have a systematic impact on explanation-based processes. In particular, people favor explanations that are simple and broad, with the consequence that engaging in explanation can shape learning and inference by leading people to seek patterns and favor hypotheses that support broad and simple explanations. Given the prevalence of explanation in everyday cognition, understanding explanation is therefore crucial to understanding learning and inference. Copyright © 2016 Elsevier Ltd. All rights reserved.
Fuzzy logic controller using different inference methods
International Nuclear Information System (INIS)
Liu, Z.; De Keyser, R.
1994-01-01
In this paper the design of fuzzy controllers by using different inference methods is introduced. Configuration of the fuzzy controllers includes a general rule-base which is a collection of fuzzy PI or PD rules, the triangular fuzzy data model and a centre of gravity defuzzification algorithm. The generalized modus ponens (GMP) is used with the minimum operator of the triangular norm. Under the sup-min inference rule, six fuzzy implication operators are employed to calculate the fuzzy look-up tables for each rule base. The performance is tested in simulated systems with MATLAB/SIMULINK. Results show the effects of using the fuzzy controllers with different inference methods and applied to different test processes
Uncertainty in prediction and in inference
International Nuclear Information System (INIS)
Hilgevoord, J.; Uffink, J.
1991-01-01
The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close relationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in inference can be obtained by means of the so-called statistical distance between probability distributions. When applied to quantum mechanics, this distance leads to a measure of the distinguishability of quantum states, which essentially is the absolute value of the matrix element between the states. The importance of this result to the quantum mechanical uncertainty principle is noted. The second part of the paper provides a derivation of the statistical distance on the basis of the so-called method of support
A Learning Algorithm for Multimodal Grammar Inference.
D'Ulizia, A; Ferri, F; Grifoni, P
2011-12-01
The high costs of development and maintenance of multimodal grammars in integrating and understanding input in multimodal interfaces lead to the investigation of novel algorithmic solutions in automating grammar generation and in updating processes. Many algorithms for context-free grammar inference have been developed in the natural language processing literature. An extension of these algorithms toward the inference of multimodal grammars is necessary for multimodal input processing. In this paper, we propose a novel grammar inference mechanism that allows us to learn a multimodal grammar from its positive samples of multimodal sentences. The algorithm first generates the multimodal grammar that is able to parse the positive samples of sentences and, afterward, makes use of two learning operators and the minimum description length metrics in improving the grammar description and in avoiding the over-generalization problem. The experimental results highlight the acceptable performances of the algorithm proposed in this paper since it has a very high probability of parsing valid sentences.
Examples in parametric inference with R
Dixit, Ulhas Jayram
2016-01-01
This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory cou...
Grammatical inference algorithms, routines and applications
Wieczorek, Wojciech
2017-01-01
This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed. Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others. Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>.
Statistical inference based on divergence measures
Pardo, Leandro
2005-01-01
The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, prese...
Improved Inference of Heteroscedastic Fixed Effects Models
Directory of Open Access Journals (Sweden)
Afshan Saeed
2016-12-01
Full Text Available Heteroscedasticity is a stern problem that distorts estimation and testing of panel data model (PDM. Arellano (1987 proposed the White (1980 estimator for PDM with heteroscedastic errors but it provides erroneous inference for the data sets including high leverage points. In this paper, our attempt is to improve heteroscedastic consistent covariance matrix estimator (HCCME for panel dataset with high leverage points. To draw robust inference for the PDM, our focus is to improve kernel bootstrap estimators, proposed by Racine and MacKinnon (2007. The Monte Carlo scheme is used for assertion of the results.
Likelihood inference for unions of interacting discs
DEFF Research Database (Denmark)
Møller, Jesper; Helisova, K.
2010-01-01
This is probably the first paper which discusses likelihood inference for a random set using a germ-grain model, where the individual grains are unobservable, edge effects occur and other complications appear. We consider the case where the grains form a disc process modelled by a marked point...... process, where the germs are the centres and the marks are the associated radii of the discs. We propose to use a recent parametric class of interacting disc process models, where the minimal sufficient statistic depends on various geometric properties of the random set, and the density is specified......-based maximum likelihood inference and the effect of specifying different reference Poisson models....
IMAGINE: Interstellar MAGnetic field INference Engine
Steininger, Theo
2018-03-01
IMAGINE (Interstellar MAGnetic field INference Engine) performs inference on generic parametric models of the Galaxy. The modular open source framework uses highly optimized tools and technology such as the MultiNest sampler (ascl:1109.006) and the information field theory framework NIFTy (ascl:1302.013) to create an instance of the Milky Way based on a set of parameters for physical observables, using Bayesian statistics to judge the mismatch between measured data and model prediction. The flexibility of the IMAGINE framework allows for simple refitting for newly available data sets and makes state-of-the-art Bayesian methods easily accessible particularly for random components of the Galactic magnetic field.
Inferring causality from noisy time series data
DEFF Research Database (Denmark)
Mønster, Dan; Fusaroli, Riccardo; Tylén, Kristian
2016-01-01
Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength...... and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise...
The Accuracy of Inference in Small Samples of Dynamic Panel Data Models
Bun, M.J.G.; Kiviet, J.F.
2001-01-01
Through Monte Carlo experiments the small sample behavior is examined of various inference techniques for dynamic panel data models when both the time-series and cross-section dimensions of the data set are small. The LSDV technique and corrected versions of it are compared with IV and GMM
Model averaging, optimal inference and habit formation
Directory of Open Access Journals (Sweden)
Thomas H B FitzGerald
2014-06-01
Full Text Available Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge – that of determining which model or models of their environment are the best for guiding behaviour. Bayesian model averaging – which says that an agent should weight the predictions of different models according to their evidence – provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent’s behaviour should show an equivalent balance. We hypothesise that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realisable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behaviour. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded Bayesian inference, focussing particularly upon the relationship between goal-directed and habitual behaviour.
Efficient Bayesian inference for ARFIMA processes
Graves, T.; Gramacy, R. B.; Franzke, C. L. E.; Watkins, N. W.
2015-03-01
Many geophysical quantities, like atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long-range dependence (LRD). LRD means that these quantities experience non-trivial temporal memory, which potentially enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LRD. In this paper we present a modern and systematic approach to the inference of LRD. Rather than Mandelbrot's fractional Gaussian noise, we use the more flexible Autoregressive Fractional Integrated Moving Average (ARFIMA) model which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LRD, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g. short memory effects) can be integrated over in order to focus on long memory parameters, and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data, with favorable comparison to the standard estimators.
Campbell's and Rubin's Perspectives on Causal Inference
West, Stephen G.; Thoemmes, Felix
2010-01-01
Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…
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.
HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR
International Nuclear Information System (INIS)
Schneider, Michael D.; Dawson, William A.; Hogg, David W.; Marshall, Philip J.; Bard, Deborah J.; Meyers, Joshua; Lang, Dustin
2015-01-01
Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics
Interest, Inferences, and Learning from Texts
Clinton, Virginia; van den Broek, Paul
2012-01-01
Topic interest and learning from texts have been found to be positively associated with each other. However, the reason for this positive association is not well understood. The purpose of this study is to examine a cognitive process, inference generation, that could explain the positive association between interest and learning from texts. In…
Ignorability in Statistical and Probabilistic Inference
DEFF Research Database (Denmark)
Jaeger, Manfred
2005-01-01
When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed...
Inverse Ising inference with correlated samples
International Nuclear Information System (INIS)
Obermayer, Benedikt; Levine, Erel
2014-01-01
Correlations between two variables of a high-dimensional system can be indicative of an underlying interaction, but can also result from indirect effects. Inverse Ising inference is a method to distinguish one from the other. Essentially, the parameters of the least constrained statistical model are learned from the observed correlations such that direct interactions can be separated from indirect correlations. Among many other applications, this approach has been helpful for protein structure prediction, because residues which interact in the 3D structure often show correlated substitutions in a multiple sequence alignment. In this context, samples used for inference are not independent but share an evolutionary history on a phylogenetic tree. Here, we discuss the effects of correlations between samples on global inference. Such correlations could arise due to phylogeny but also via other slow dynamical processes. We present a simple analytical model to address the resulting inference biases, and develop an exact method accounting for background correlations in alignment data by combining phylogenetic modeling with an adaptive cluster expansion algorithm. We find that popular reweighting schemes are only marginally effective at removing phylogenetic bias, suggest a rescaling strategy that yields better results, and provide evidence that our conclusions carry over to the frequently used mean-field approach to the inverse Ising problem. (paper)
Evolutionary inference via the Poisson Indel Process.
Bouchard-Côté, Alexandre; Jordan, Michael I
2013-01-22
We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.
Culture and Pragmatic Inference in Interpersonal Communication
African Journals Online (AJOL)
cognitive process, and that the human capacity for inference is crucially important ... been noted that research in interpersonal communication is currently pushing the ... communicative actions, the social-cultural world of everyday life is not only ... personal experiences of the authors', as documented over time and recreated ...
Inference and the Introductory Statistics Course
Pfannkuch, Maxine; Regan, Matt; Wild, Chris; Budgett, Stephanie; Forbes, Sharleen; Harraway, John; Parsonage, Ross
2011-01-01
This article sets out some of the rationale and arguments for making major changes to the teaching and learning of statistical inference in introductory courses at our universities by changing from a norm-based, mathematical approach to more conceptually accessible computer-based approaches. The core problem of the inferential argument with its…
Statistical Inference on the Canadian Middle Class
Directory of Open Access Journals (Sweden)
Russell Davidson
2018-03-01
Full Text Available Conventional wisdom says that the middle classes in many developed countries have recently suffered losses, in terms of both the share of the total population belonging to the middle class, and also their share in total income. Here, distribution-free methods are developed for inference on these shares, by means of deriving expressions for their asymptotic variances of sample estimates, and the covariance of the estimates. Asymptotic inference can be undertaken based on asymptotic normality. Bootstrap inference can be expected to be more reliable, and appropriate bootstrap procedures are proposed. As an illustration, samples of individual earnings drawn from Canadian census data are used to test various hypotheses about the middle-class shares, and confidence intervals for them are computed. It is found that, for the earlier censuses, sample sizes are large enough for asymptotic and bootstrap inference to be almost identical, but that, in the twenty-first century, the bootstrap fails on account of a strange phenomenon whereby many presumably different incomes in the data are rounded to one and the same value. Another difference between the centuries is the appearance of heavy right-hand tails in the income distributions of both men and women.
Spurious correlations and inference in landscape genetics
Samuel A. Cushman; Erin L. Landguth
2010-01-01
Reliable interpretation of landscape genetic analyses depends on statistical methods that have high power to identify the correct process driving gene flow while rejecting incorrect alternative hypotheses. Little is known about statistical power and inference in individual-based landscape genetics. Our objective was to evaluate the power of causalmodelling with partial...
Cortical information flow during inferences of agency
Dogge, Myrthel; Hofman, Dennis; Boersma, Maria; Dijkerman, H Chris; Aarts, Henk
2014-01-01
Building on the recent finding that agency experiences do not merely rely on sensorimotor information but also on cognitive cues, this exploratory study uses electroencephalographic recordings to examine functional connectivity during agency inference processing in a setting where action and outcome
Quasi-Experimental Designs for Causal Inference
Kim, Yongnam; Steiner, Peter
2016-01-01
When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…
The importance of learning when making inferences
Directory of Open Access Journals (Sweden)
Jorg Rieskamp
2008-03-01
Full Text Available The assumption that people possess a repertoire of strategies to solve the inference problems they face has been made repeatedly. The experimental findings of two previous studies on strategy selection are reexamined from a learning perspective, which argues that people learn to select strategies for making probabilistic inferences. This learning process is modeled with the strategy selection learning (SSL theory, which assumes that people develop subjective expectancies for the strategies they have. They select strategies proportional to their expectancies, which are updated on the basis of experience. For the study by Newell, Weston, and Shanks (2003 it can be shown that people did not anticipate the success of a strategy from the beginning of the experiment. Instead, the behavior observed at the end of the experiment was the result of a learning process that can be described by the SSL theory. For the second study, by Br"oder and Schiffer (2006, the SSL theory is able to provide an explanation for why participants only slowly adapted to new environments in a dynamic inference situation. The reanalysis of the previous studies illustrates the importance of learning for probabilistic inferences.
Colligation, Or the Logical Inference of Interconnection
DEFF Research Database (Denmark)
Falster, Peter
1998-01-01
laws or assumptions. Yet interconnection as an abstract concept seems to be without scientific underpinning in pure logic. Adopting a historical viewpoint, our aim is to show that the reasoning of interconnection may be identified with a neglected kind of logical inference, called "colligation...
Colligation or, The Logical Inference of Interconnection
DEFF Research Database (Denmark)
Franksen, Ole Immanuel; Falster, Peter
2000-01-01
laws or assumptions. Yet interconnection as an abstract concept seems to be without scientific underpinning in oure logic. Adopting a historical viewpoint, our aim is to show that the reasoning of interconnection may be identified with a neglected kind of logical inference, called "colligation...
Inferring motion and location using WLAN RSSI
Kavitha Muthukrishnan, K.; van der Zwaag, B.J.; Havinga, Paul J.M.; Fuller, R.; Koutsoukos, X.
2009-01-01
We present novel algorithms to infer movement by making use of inherent fluctuations in the received signal strengths from existing WLAN infrastructure. We evaluate the performance of the presented algorithms based on classification metrics such as recall and precision using annotated traces
Active inference, sensory attenuation and illusions.
Brown, Harriet; Adams, Rick A; Parees, Isabel; Edwards, Mark; Friston, Karl
2013-11-01
Active inference provides a simple and neurobiologically plausible account of how action and perception are coupled in producing (Bayes) optimal behaviour. This can be seen most easily as minimising prediction error: we can either change our predictions to explain sensory input through perception. Alternatively, we can actively change sensory input to fulfil our predictions. In active inference, this action is mediated by classical reflex arcs that minimise proprioceptive prediction error created by descending proprioceptive predictions. However, this creates a conflict between action and perception; in that, self-generated movements require predictions to override the sensory evidence that one is not actually moving. However, ignoring sensory evidence means that externally generated sensations will not be perceived. Conversely, attending to (proprioceptive and somatosensory) sensations enables the detection of externally generated events but precludes generation of actions. This conflict can be resolved by attenuating the precision of sensory evidence during movement or, equivalently, attending away from the consequences of self-made acts. We propose that this Bayes optimal withdrawal of precise sensory evidence during movement is the cause of psychophysical sensory attenuation. Furthermore, it explains the force-matching illusion and reproduces empirical results almost exactly. Finally, if attenuation is removed, the force-matching illusion disappears and false (delusional) inferences about agency emerge. This is important, given the negative correlation between sensory attenuation and delusional beliefs in normal subjects--and the reduction in the magnitude of the illusion in schizophrenia. Active inference therefore links the neuromodulatory optimisation of precision to sensory attenuation and illusory phenomena during the attribution of agency in normal subjects. It also provides a functional account of deficits in syndromes characterised by false inference
Likelihood inference for unions of interacting discs
DEFF Research Database (Denmark)
Møller, Jesper; Helisová, Katarina
To the best of our knowledge, this is the first paper which discusses likelihood inference or a random set using a germ-grain model, where the individual grains are unobservable edge effects occur, and other complications appear. We consider the case where the grains form a disc process modelled...... is specified with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analyzing Peter Diggle's heather dataset, where we discuss the results...... of simulation-based maximum likelihood inference and the effect of specifying different reference Poisson models....
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++
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.
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.
Dopamine, reward learning, and active inference
Directory of Open Access Journals (Sweden)
Thomas eFitzgerald
2015-11-01
Full Text Available Temporal difference learning models propose phasic dopamine signalling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behaviour. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.
Dopamine, reward learning, and active inference.
FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl
2015-01-01
Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.
Inferring genetic interactions from comparative fitness data.
Crona, Kristina; Gavryushkin, Alex; Greene, Devin; Beerenwinkel, Niko
2017-12-20
Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax , the fungus Aspergillus niger , and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.
An emergent approach to analogical inference
Thibodeau, Paul H.; Flusberg, Stephen J.; Glick, Jeremy J.; Sternberg, Daniel A.
2013-03-01
In recent years, a growing number of researchers have proposed that analogy is a core component of human cognition. According to the dominant theoretical viewpoint, analogical reasoning requires a specific suite of cognitive machinery, including explicitly coded symbolic representations and a mapping or binding mechanism that operates over these representations. Here we offer an alternative approach: we find that analogical inference can emerge naturally and spontaneously from a relatively simple, error-driven learning mechanism without the need to posit any additional analogy-specific machinery. The results also parallel findings from the developmental literature on analogy, demonstrating a shift from an initial reliance on surface feature similarity to the use of relational similarity later in training. Variants of the model allow us to consider and rule out alternative accounts of its performance. We conclude by discussing how these findings can potentially refine our understanding of the processes that are required to perform analogical inference.
Pointwise probability reinforcements for robust statistical inference.
Frénay, Benoît; Verleysen, Michel
2014-02-01
Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation. Copyright © 2013 Elsevier Ltd. All rights reserved.
Statistical inference from imperfect photon detection
International Nuclear Information System (INIS)
Audenaert, Koenraad M R; Scheel, Stefan
2009-01-01
We consider the statistical properties of photon detection with imperfect detectors that exhibit dark counts and less than unit efficiency, in the context of tomographic reconstruction. In this context, the detectors are used to implement certain positive operator-valued measures (POVMs) that would allow us to reconstruct the quantum state or quantum process under consideration. Here we look at the intermediate step of inferring outcome probabilities from measured outcome frequencies, and show how this inference can be performed in a statistically sound way in the presence of detector imperfections. Merging outcome probabilities for different sets of POVMs into a consistent quantum state picture has been treated elsewhere (Audenaert and Scheel 2009 New J. Phys. 11 023028). Single-photon pulsed measurements as well as continuous wave measurements are covered.
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++.
Working with sample data exploration and inference
Chaffe-Stengel, Priscilla
2014-01-01
Managers and analysts routinely collect and examine key performance measures to better understand their operations and make good decisions. Being able to render the complexity of operations data into a coherent account of significant events requires an understanding of how to work well with raw data and to make appropriate inferences. Although some statistical techniques for analyzing data and making inferences are sophisticated and require specialized expertise, there are methods that are understandable and applicable by anyone with basic algebra skills and the support of a spreadsheet package. By applying these fundamental methods themselves rather than turning over both the data and the responsibility for analysis and interpretation to an expert, managers will develop a richer understanding and potentially gain better control over their environment. This text is intended to describe these fundamental statistical techniques to managers, data analysts, and students. Statistical analysis of sample data is enh...
Parametric inference for biological sequence analysis.
Pachter, Lior; Sturmfels, Bernd
2004-11-16
One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.
Inferences on Children’s Reading Groups
Directory of Open Access Journals (Sweden)
Javier González García
2009-05-01
Full Text Available This article focuses on the non-literal information of a text, which can be inferred from key elements or clues offered by the text itself. This kind of text is called implicit text or inference, due to the thinking process that it stimulates. The explicit resources that lead to information retrieval are related to others of implicit information, which have increased their relevance. In this study, during two courses, how two teachers interpret three stories and how they establish a debate dividing the class into three student groups, was analyzed. The sample was formed by two classes of two urban public schools of Burgos capital (Spain, and two of public schools of Tampico (Mexico. This allowed us to observe an increasing percentage value of the group focused in text comprehension, and a lesser percentage of the group perceiving comprehension as a secondary objective.
Inferring Genetic Ancestry: Opportunities, Challenges, and Implications
Royal, Charmaine D.; Novembre, John; Fullerton, Stephanie M.; Goldstein, David B.; Long, Jeffrey C.; Bamshad, Michael J.; Clark, Andrew G.
2010-01-01
Increasing public interest in direct-to-consumer (DTC) genetic ancestry testing has been accompanied by growing concern about issues ranging from the personal and societal implications of the testing to the scientific validity of ancestry inference. The very concept of “ancestry” is subject to misunderstanding in both the general and scientific communities. What do we mean by ancestry? How exactly is ancestry measured? How far back can such ancestry be defined and by which genetic tools? How ...
Spatial Inference Based on Geometric Proportional Analogies
Mullally, Emma-Claire; O'Donoghue, Diarmuid P.
2006-01-01
We describe an instance-based reasoning solution to a variety of spatial reasoning problems. The solution centers on identifying an isomorphic mapping between labelled graphs that represent some problem data and a known solution instance. We describe a number of spatial reasoning problems that are solved by generating non-deductive inferences, integrating topology with area (and other) features. We report the accuracy of our algorithm on different categories of spatial reasoning tasks from th...
Inferring ontology graph structures using OWL reasoning
Rodriguez-Garcia, Miguel Angel
2018-01-05
Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies\\' semantic content remains a challenge.We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies\\' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph .Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.
Role of Speaker Cues in Attention Inference
Jin Joo Lee; Cynthia Breazeal; David DeSteno
2017-01-01
Current state-of-the-art approaches to emotion recognition primarily focus on modeling the nonverbal expressions of the sole individual without reference to contextual elements such as the co-presence of the partner. In this paper, we demonstrate that the accurate inference of listeners’ social-emotional state of attention depends on accounting for the nonverbal behaviors of their storytelling partner, namely their speaker cues. To gain a deeper understanding of the role of speaker cues in at...
Inferring ontology graph structures using OWL reasoning.
Rodríguez-García, Miguel Ángel; Hoehndorf, Robert
2018-01-05
Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.
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 ...
Using metacognitive cues to infer others' thinking
André Mata; Tiago Almeida
2014-01-01
Three studies tested whether people use cues about the way other people think---for example, whether others respond fast vs. slow---to infer what responses other people might give to reasoning problems. People who solve reasoning problems using deliberative thinking have better insight than intuitive problem-solvers into the responses that other people might give to the same problems. Presumably because deliberative responders think of intuitive responses before they think o...
Thermodynamics of statistical inference by cells.
Lang, Alex H; Fisher, Charles K; Mora, Thierry; Mehta, Pankaj
2014-10-03
The deep connection between thermodynamics, computation, and information is now well established both theoretically and experimentally. Here, we extend these ideas to show that thermodynamics also places fundamental constraints on statistical estimation and learning. To do so, we investigate the constraints placed by (nonequilibrium) thermodynamics on the ability of biochemical signaling networks to estimate the concentration of an external signal. We show that accuracy is limited by energy consumption, suggesting that there are fundamental thermodynamic constraints on statistical inference.
Bootstrap inference when using multiple imputation.
Schomaker, Michael; Heumann, Christian
2018-04-16
Many modern estimators require bootstrapping to calculate confidence intervals because either no analytic standard error is available or the distribution of the parameter of interest is nonsymmetric. It remains however unclear how to obtain valid bootstrap inference when dealing with multiple imputation to address missing data. We present 4 methods that are intuitively appealing, easy to implement, and combine bootstrap estimation with multiple imputation. We show that 3 of the 4 approaches yield valid inference, but that the performance of the methods varies with respect to the number of imputed data sets and the extent of missingness. Simulation studies reveal the behavior of our approaches in finite samples. A topical analysis from HIV treatment research, which determines the optimal timing of antiretroviral treatment initiation in young children, demonstrates the practical implications of the 4 methods in a sophisticated and realistic setting. This analysis suffers from missing data and uses the g-formula for inference, a method for which no standard errors are available. Copyright © 2018 John Wiley & Sons, Ltd.
Inferring epidemic network topology from surveillance data.
Directory of Open Access Journals (Sweden)
Xiang Wan
Full Text Available The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases.
Role of Speaker Cues in Attention Inference
Directory of Open Access Journals (Sweden)
Jin Joo Lee
2017-10-01
Full Text Available Current state-of-the-art approaches to emotion recognition primarily focus on modeling the nonverbal expressions of the sole individual without reference to contextual elements such as the co-presence of the partner. In this paper, we demonstrate that the accurate inference of listeners’ social-emotional state of attention depends on accounting for the nonverbal behaviors of their storytelling partner, namely their speaker cues. To gain a deeper understanding of the role of speaker cues in attention inference, we conduct investigations into real-world interactions of children (5–6 years old storytelling with their peers. Through in-depth analysis of human–human interaction data, we first identify nonverbal speaker cues (i.e., backchannel-inviting cues and listener responses (i.e., backchannel feedback. We then demonstrate how speaker cues can modify the interpretation of attention-related backchannels as well as serve as a means to regulate the responsiveness of listeners. We discuss the design implications of our findings toward our primary goal of developing attention recognition models for storytelling robots, and we argue that social robots can proactively use speaker cues to form more accurate inferences about the attentive state of their human partners.
Cortical information flow during inferences of agency
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Myrthel eDogge
2014-08-01
Full Text Available Building on the recent finding that agency experiences do not merely rely on sensorimotor information but also on cognitive cues, this exploratory study uses electroencephalographic recordings to examine functional connectivity during agency inference processing in a setting where action and outcome are independent. Participants completed a computerized task in which they pressed a button followed by one of two color words (red or blue and rated their experienced agency over producing the color. Before executing the action, a matching or mismatching color word was pre-activated by explicitly instructing participants to produce the color (goal condition or by briefly presenting the color word (prime condition. In both conditions, experienced agency was higher in matching versus mismatching trials. Furthermore, increased electroencephalography (EEG-based connectivity strength was observed between parietal and frontal nodes and within the (prefrontal cortex when color-outcomes matched with goals and participants reported high agency. This pattern of increased connectivity was not identified in trials where outcomes were pre-activated through primes. These results suggest that different connections are involved in the experience and in the loss of agency, as well as in inferences of agency resulting from different types of pre-activation. Moreover, the findings provide novel support for the involvement of a fronto-parietal network in agency inferences.
Phylogenetic Inference of HIV Transmission Clusters
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Vlad Novitsky
2017-10-01
Full Text Available Better understanding the structure and dynamics of HIV transmission networks is essential for designing the most efficient interventions to prevent new HIV transmissions, and ultimately for gaining control of the HIV epidemic. The inference of phylogenetic relationships and the interpretation of results rely on the definition of the HIV transmission cluster. The definition of the HIV cluster is complex and dependent on multiple factors, including the design of sampling, accuracy of sequencing, precision of sequence alignment, evolutionary models, the phylogenetic method of inference, and specified thresholds for cluster support. While the majority of studies focus on clusters, non-clustered cases could also be highly informative. A new dimension in the analysis of the global and local HIV epidemics is the concept of phylogenetically distinct HIV sub-epidemics. The identification of active HIV sub-epidemics reveals spreading viral lineages and may help in the design of targeted interventions.HIVclustering can also be affected by sampling density. Obtaining a proper sampling density may increase statistical power and reduce sampling bias, so sampling density should be taken into account in study design and in interpretation of phylogenetic results. Finally, recent advances in long-range genotyping may enable more accurate inference of HIV transmission networks. If performed in real time, it could both inform public-health strategies and be clinically relevant (e.g., drug-resistance testing.
Causal inference of asynchronous audiovisual speech
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John F Magnotti
2013-11-01
Full Text Available During speech perception, humans integrate auditory information from the voice with visual information from the face. This multisensory integration increases perceptual precision, but only if the two cues come from the same talker; this requirement has been largely ignored by current models of speech perception. We describe a generative model of multisensory speech perception that includes this critical step of determining the likelihood that the voice and face information have a common cause. A key feature of the model is that it is based on a principled analysis of how an observer should solve this causal inference problem using the asynchrony between two cues and the reliability of the cues. This allows the model to make predictions abut the behavior of subjects performing a synchrony judgment task, predictive power that does not exist in other approaches, such as post hoc fitting of Gaussian curves to behavioral data. We tested the model predictions against the performance of 37 subjects performing a synchrony judgment task viewing audiovisual speech under a variety of manipulations, including varying asynchronies, intelligibility, and visual cue reliability. The causal inference model outperformed the Gaussian model across two experiments, providing a better fit to the behavioral data with fewer parameters. Because the causal inference model is derived from a principled understanding of the task, model parameters are directly interpretable in terms of stimulus and subject properties.
Functional neuroanatomy of intuitive physical inference.
Fischer, Jason; Mikhael, John G; Tenenbaum, Joshua B; Kanwisher, Nancy
2016-08-23
To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.
Elements of Causal Inference: Foundations and Learning Algorithms
DEFF Research Database (Denmark)
Peters, Jonas Martin; Janzing, Dominik; Schölkopf, Bernhard
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning......A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning...
Integrating distributed Bayesian inference and reinforcement learning for sensor management
Grappiolo, C.; Whiteson, S.; Pavlin, G.; Bakker, B.
2009-01-01
This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically
Bootstrapping phylogenies inferred from rearrangement data
Directory of Open Access Journals (Sweden)
Lin Yu
2012-08-01
Full Text Available Abstract Background Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed over the last few years and have found use in comparative genomics and phylogenetic inference. However, the assessment of phylogenies built from such data has not been properly addressed to date. The standard method used in sequence-based phylogenetic inference is the bootstrap, but it relies on a large number of homologous characters that can be resampled; yet in the case of rearrangements, the entire genome is a single character. Alternatives such as the jackknife suffer from the same problem, while likelihood tests cannot be applied in the absence of well established probabilistic models. Results We present a new approach to the assessment of distance-based phylogenetic inference from whole-genome data; our approach combines features of the jackknife and the bootstrap and remains nonparametric. For each feature of our method, we give an equivalent feature in the sequence-based framework; we also present the results of extensive experimental testing, in both sequence-based and genome-based frameworks. Through the feature-by-feature comparison and the experimental results, we show that our bootstrapping approach is on par with the classic phylogenetic bootstrap used in sequence-based reconstruction, and we establish the clear superiority of the classic bootstrap for sequence data and of our corresponding new approach for rearrangement data over proposed variants. Finally, we test our approach on a small dataset of mammalian genomes, verifying that the support values match current thinking about the respective branches. Conclusions Our method is the first to provide a standard of assessment to match that of the classic phylogenetic bootstrap for aligned sequences. Its
Bootstrapping phylogenies inferred from rearrangement data.
Lin, Yu; Rajan, Vaibhav; Moret, Bernard Me
2012-08-29
Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed over the last few years and have found use in comparative genomics and phylogenetic inference. However, the assessment of phylogenies built from such data has not been properly addressed to date. The standard method used in sequence-based phylogenetic inference is the bootstrap, but it relies on a large number of homologous characters that can be resampled; yet in the case of rearrangements, the entire genome is a single character. Alternatives such as the jackknife suffer from the same problem, while likelihood tests cannot be applied in the absence of well established probabilistic models. We present a new approach to the assessment of distance-based phylogenetic inference from whole-genome data; our approach combines features of the jackknife and the bootstrap and remains nonparametric. For each feature of our method, we give an equivalent feature in the sequence-based framework; we also present the results of extensive experimental testing, in both sequence-based and genome-based frameworks. Through the feature-by-feature comparison and the experimental results, we show that our bootstrapping approach is on par with the classic phylogenetic bootstrap used in sequence-based reconstruction, and we establish the clear superiority of the classic bootstrap for sequence data and of our corresponding new approach for rearrangement data over proposed variants. Finally, we test our approach on a small dataset of mammalian genomes, verifying that the support values match current thinking about the respective branches. Our method is the first to provide a standard of assessment to match that of the classic phylogenetic bootstrap for aligned sequences. Its support values follow a similar scale and its receiver
Type Inference for Session Types in the Pi-Calculus
DEFF Research Database (Denmark)
Graversen, Eva Fajstrup; Harbo, Jacob Buchreitz; Huttel, Hans
2014-01-01
In this paper we present a direct algorithm for session type inference for the π-calculus. Type inference for session types has previously been achieved by either imposing limitations and restriction on the π-calculus, or by reducing the type inference problem to that for linear types. Our approach...
Reasoning about Informal Statistical Inference: One Statistician's View
Rossman, Allan J.
2008-01-01
This paper identifies key concepts and issues associated with the reasoning of informal statistical inference. I focus on key ideas of inference that I think all students should learn, including at secondary level as well as tertiary. I argue that a fundamental component of inference is to go beyond the data at hand, and I propose that statistical…
Statistical Inference at Work: Statistical Process Control as an Example
Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia
2008-01-01
To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…
Malle, Bertram F; Holbrook, Jess
2012-04-01
People interpret behavior by making inferences about agents' intentionality, mind, and personality. Past research studied such inferences 1 at a time; in real life, people make these inferences simultaneously. The present studies therefore examined whether 4 major inferences (intentionality, desire, belief, and personality), elicited simultaneously in response to an observed behavior, might be ordered in a hierarchy of likelihood and speed. To achieve generalizability, the studies included a wide range of stimulus behaviors, presented them verbally and as dynamic videos, and assessed inferences both in a retrieval paradigm (measuring the likelihood and speed of accessing inferences immediately after they were made) and in an online processing paradigm (measuring the speed of forming inferences during behavior observation). Five studies provide evidence for a hierarchy of social inferences-from intentionality and desire to belief to personality-that is stable across verbal and visual presentations and that parallels the order found in developmental and primate research. (c) 2012 APA, all rights reserved.
Hofmann, B
2008-06-01
Are there similarities between scientific and moral inference? This is the key question in this article. It takes as its point of departure an instance of one person's story in the media changing both Norwegian public opinion and a brand-new Norwegian law prohibiting the use of saviour siblings. The case appears to falsify existing norms and to establish new ones. The analysis of this case reveals similarities in the modes of inference in science and morals, inasmuch as (a) a single case functions as a counter-example to an existing rule; (b) there is a common presupposition of stability, similarity and order, which makes it possible to reason from a few cases to a general rule; and (c) this makes it possible to hold things together and retain order. In science, these modes of inference are referred to as falsification, induction and consistency. In morals, they have a variety of other names. Hence, even without abandoning the fact-value divide, there appear to be similarities between inference in science and inference in morals, which may encourage communication across the boundaries between "the two cultures" and which are relevant to medical humanities.
Nonparametric inference of network structure and dynamics
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among
Impact of noise on molecular network inference.
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Radhakrishnan Nagarajan
Full Text Available Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a common-effect, (b three-chain, and (c coherent type-I feed-forward loop (FFL are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS and Incremental Association Markov Blanket (IAMB that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the
Bayesian Estimation and Inference using Stochastic Hardware
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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.
Pillow, Bradford H; Pearson, Raeanne M; Hecht, Mary; Bremer, Amanda
2010-01-01
Children and adults rated their own certainty following inductive inferences, deductive inferences, and guesses. Beginning in kindergarten, participants rated deductions as more certain than weak inductions or guesses. Deductions were rated as more certain than strong inductions beginning in Grade 3, and fourth-grade children and adults differentiated strong inductions, weak inductions, and informed guesses from pure guesses. By Grade 3, participants also gave different types of explanations for their deductions and inductions. These results are discussed in relation to children's concepts of cognitive processes, logical reasoning, and epistemological development.
Robust Inference with Multi-way Clustering
A. Colin Cameron; Jonah B. Gelbach; Douglas L. Miller; Doug Miller
2009-01-01
In this paper we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit and GMM. This variance estimator enables cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our...
Approximate Inference and Deep Generative Models
CERN. Geneva
2018-01-01
Advances in deep generative models are at the forefront of deep learning research because of the promise they offer for allowing data-efficient learning, and for model-based reinforcement learning. In this talk I'll review a few standard methods for approximate inference and introduce modern approximations which allow for efficient large-scale training of a wide variety of generative models. Finally, I'll demonstrate several important application of these models to density estimation, missing data imputation, data compression and planning.
Abductive Inference using Array-Based Logic
DEFF Research Database (Denmark)
Frisvad, Jeppe Revall; Falster, Peter; Møller, Gert L.
The notion of abduction has found its usage within a wide variety of AI fields. Computing abductive solutions has, however, shown to be highly intractable in logic programming. To avoid this intractability we present a new approach to logicbased abduction; through the geometrical view of data...... employed in array-based logic we embrace abduction in a simple structural operation. We argue that a theory of abduction on this form allows for an implementation which, at runtime, can perform abductive inference quite efficiently on arbitrary rules of logic representing knowledge of finite domains....
DEFF Research Database (Denmark)
Andersen, Jesper; Lawall, Julia Laetitia
2008-01-01
A key issue in maintaining Linux device drivers is the need to update drivers in response to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spfind, that identifies common changes made...... developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect...
Inverse Ising Inference Using All the Data
Aurell, Erik; Ekeberg, Magnus
2012-03-01
We show that a method based on logistic regression, using all the data, solves the inverse Ising problem far better than mean-field calculations relying only on sample pairwise correlation functions, while still computationally feasible for hundreds of nodes. The largest improvement in reconstruction occurs for strong interactions. Using two examples, a diluted Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that interaction topologies can be recovered from few samples with good accuracy and that the use of l1 regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.
Quantum Enhanced Inference in Markov Logic Networks.
Wittek, Peter; Gogolin, Christian
2017-04-19
Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.
Inferring network topology from complex dynamics
International Nuclear Information System (INIS)
Shandilya, Srinivas Gorur; Timme, Marc
2011-01-01
Inferring the network topology from dynamical observations is a fundamental problem pervading research on complex systems. Here, we present a simple, direct method for inferring the structural connection topology of a network, given an observation of one collective dynamical trajectory. The general theoretical framework is applicable to arbitrary network dynamical systems described by ordinary differential equations. No interference (external driving) is required and the type of dynamics is hardly restricted in any way. In particular, the observed dynamics may be arbitrarily complex; stationary, invariant or transient; synchronous or asynchronous and chaotic or periodic. Presupposing a knowledge of the functional form of the dynamical units and of the coupling functions between them, we present an analytical solution to the inverse problem of finding the network topology from observing a time series of state variables only. Robust reconstruction is achieved in any sufficiently long generic observation of the system. We extend our method to simultaneously reconstructing both the entire network topology and all parameters appearing linear in the system's equations of motion. Reconstruction of network topology and system parameters is viable even in the presence of external noise that distorts the original dynamics substantially. The method provides a conceptually new step towards reconstructing a variety of real-world networks, including gene and protein interaction networks and neuronal circuits.
Inferring climate sensitivity from volcanic events
Energy Technology Data Exchange (ETDEWEB)
Boer, G.J. [Environment Canada, University of Victoria, Canadian Centre for Climate Modelling and Analysis, Victoria, BC (Canada); Stowasser, M.; Hamilton, K. [University of Hawaii, International Pacific Research Centre, Honolulu, HI (United States)
2007-04-15
The possibility of estimating the equilibrium climate sensitivity of the earth-system from observations following explosive volcanic eruptions is assessed in the context of a perfect model study. Two modern climate models (the CCCma CGCM3 and the NCAR CCSM2) with different equilibrium climate sensitivities are employed in the investigation. The models are perturbed with the same transient volcano-like forcing and the responses analysed to infer climate sensitivities. For volcano-like forcing the global mean surface temperature responses of the two models are very similar, despite their differing equilibrium climate sensitivities, indicating that climate sensitivity cannot be inferred from the temperature record alone even if the forcing is known. Equilibrium climate sensitivities can be reasonably determined only if both the forcing and the change in heat storage in the system are known very accurately. The geographic patterns of clear-sky atmosphere/surface and cloud feedbacks are similar for both the transient volcano-like and near-equilibrium constant forcing simulations showing that, to a considerable extent, the same feedback processes are invoked, and determine the climate sensitivity, in both cases. (orig.)
Facility Activity Inference Using Radiation Networks
Energy Technology Data Exchange (ETDEWEB)
Rao, Nageswara S. [ORNL; Ramirez Aviles, Camila A. [ORNL
2017-11-01
We consider the problem of inferring the operational status of a reactor facility using measurements from a radiation sensor network deployed around the facility’s ventilation off-gas stack. The intensity of stack emissions decays with distance, and the sensor counts or measurements are inherently random with parameters determined by the intensity at the sensor’s location. We utilize the measurements to estimate the intensity at the stack, and use it in a one-sided Sequential Probability Ratio Test (SPRT) to infer on/off status of the reactor. We demonstrate the superior performance of this method over conventional majority fusers and individual sensors using (i) test measurements from a network of 21 NaI detectors, and (ii) effluence measurements collected at the stack of a reactor facility. We also analytically establish the superior detection performance of the network over individual sensors with fixed and adaptive thresholds by utilizing the Poisson distribution of the counts. We quantify the performance improvements of the network detection over individual sensors using the packing number of the intensity space.
Models for inference in dynamic metacommunity systems
Dorazio, Robert M.; Kery, Marc; Royle, J. Andrew; Plattner, Matthias
2010-01-01
A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species- and location-specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species-specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity.
Causal inference, probability theory, and graphical insights.
Baker, Stuart G
2013-11-10
Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M-bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK-Plot for understanding Simpson's paradox with a binary confounder, the BK2-Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD-Plot for understanding the principal stratification component of the paired availability design. Published 2013. This article is a US Government work and is in the public domain in the USA.
Inferring relevance in a changing world
Directory of Open Access Journals (Sweden)
Robert C Wilson
2012-01-01
Full Text Available Reinforcement learning models of human and animal learning usually concentrate on how we learn the relationship between different stimuli or actions and rewards. However, in real world situations stimuli are ill-defined. On the one hand, our immediate environment is extremely multi-dimensional. On the other hand, in every decision-making scenario only a few aspects of the environment are relevant for obtaining reward, while most are irrelevant. Thus a key question is how do we learn these relevant dimensions, that is, how do we learn what to learn about? We investigated this process of representation learning experimentally, using a task in which one stimulus dimension was relevant for determining reward at each point in time. As in real life situations, in our task the relevant dimension can change without warning, adding ever-present uncertainty engendered by a constantly changing environment. We show that human performance on this task is better described by a suboptimal strategy based on selective attention and serial hypothesis testing rather than a normative strategy based on probabilistic inference. From this, we conjecture that the problem of inferring relevance in general scenarios is too computationally demanding for the brain to solve optimally. As a result the brain utilizes approximations, employing these even in simplified scenarios in which optimal representation learning is tractable, such as the one in our experiment.
Automated adaptive inference of phenomenological dynamical models
Daniels, Bryan
Understanding the dynamics of biochemical systems can seem impossibly complicated at the microscopic level: detailed properties of every molecular species, including those that have not yet been discovered, could be important for producing macroscopic behavior. The profusion of data in this area has raised the hope that microscopic dynamics might be recovered in an automated search over possible models, yet the combinatorial growth of this space has limited these techniques to systems that contain only a few interacting species. We take a different approach inspired by coarse-grained, phenomenological models in physics. Akin to a Taylor series producing Hooke's Law, forgoing microscopic accuracy allows us to constrain the search over dynamical models to a single dimension. This makes it feasible to infer dynamics with very limited data, including cases in which important dynamical variables are unobserved. We name our method Sir Isaac after its ability to infer the dynamical structure of the law of gravitation given simulated planetary motion data. Applying the method to output from a microscopically complicated but macroscopically simple biological signaling model, it is able to adapt the level of detail to the amount of available data. Finally, using nematode behavioral time series data, the method discovers an effective switch between behavioral attractors after the application of a painful stimulus.
Graphical models for inferring single molecule dynamics
Directory of Open Access Journals (Sweden)
Gonzalez Ruben L
2010-10-01
Full Text Available Abstract Background The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM. The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM with Gaussian observables. A detailed description of smFRET is provided as well. Results The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME, and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML optimized by the expectation maximization (EM algorithm, the most important being a natural form of model selection and a well-posed (non-divergent optimization problem. Conclusions The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.
Quantum Enhanced Inference in Markov Logic Networks
Wittek, Peter; Gogolin, Christian
2017-04-01
Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.
Causal Inference in the Perception of Verticality.
de Winkel, Ksander N; Katliar, Mikhail; Diers, Daniel; Bülthoff, Heinrich H
2018-04-03
The perceptual upright is thought to be constructed by the central nervous system (CNS) as a vector sum; by combining estimates on the upright provided by the visual system and the body's inertial sensors with prior knowledge that upright is usually above the head. Recent findings furthermore show that the weighting of the respective sensory signals is proportional to their reliability, consistent with a Bayesian interpretation of a vector sum (Forced Fusion, FF). However, violations of FF have also been reported, suggesting that the CNS may rely on a single sensory system (Cue Capture, CC), or choose to process sensory signals based on inferred signal causality (Causal Inference, CI). We developed a novel alternative-reality system to manipulate visual and physical tilt independently. We tasked participants (n = 36) to indicate the perceived upright for various (in-)congruent combinations of visual-inertial stimuli, and compared models based on their agreement with the data. The results favor the CI model over FF, although this effect became unambiguous only for large discrepancies (±60°). We conclude that the notion of a vector sum does not provide a comprehensive explanation of the perception of the upright, and that CI offers a better alternative.
Constraint Satisfaction Inference : Non-probabilistic Global Inference for Sequence Labelling
Canisius, S.V.M.; van den Bosch, A.; Daelemans, W.; Basili, R.; Moschitti, A.
2006-01-01
We present a new method for performing sequence labelling based on the idea of using a machine-learning classifier to generate several possible output sequences, and then applying an inference procedure to select the best sequence among those. Most sequence labelling methods following a similar
Human brain lesion-deficit inference remapped.
Mah, Yee-Haur; Husain, Masud; Rees, Geraint; Nachev, Parashkev
2014-09-01
Our knowledge of the anatomical organization of the human brain in health and disease draws heavily on the study of patients with focal brain lesions. Historically the first method of mapping brain function, it is still potentially the most powerful, establishing the necessity of any putative neural substrate for a given function or deficit. Great inferential power, however, carries a crucial vulnerability: without stronger alternatives any consistent error cannot be easily detected. A hitherto unexamined source of such error is the structure of the high-dimensional distribution of patterns of focal damage, especially in ischaemic injury-the commonest aetiology in lesion-deficit studies-where the anatomy is naturally shaped by the architecture of the vascular tree. This distribution is so complex that analysis of lesion data sets of conventional size cannot illuminate its structure, leaving us in the dark about the presence or absence of such error. To examine this crucial question we assembled the largest known set of focal brain lesions (n = 581), derived from unselected patients with acute ischaemic injury (mean age = 62.3 years, standard deviation = 17.8, male:female ratio = 0.547), visualized with diffusion-weighted magnetic resonance imaging, and processed with validated automated lesion segmentation routines. High-dimensional analysis of this data revealed a hidden bias within the multivariate patterns of damage that will consistently distort lesion-deficit maps, displacing inferred critical regions from their true locations, in a manner opaque to replication. Quantifying the size of this mislocalization demonstrates that past lesion-deficit relationships estimated with conventional inferential methodology are likely to be significantly displaced, by a magnitude dependent on the unknown underlying lesion-deficit relationship itself. Past studies therefore cannot be retrospectively corrected, except by new knowledge that would render them redundant
Meta-learning framework applied in bioinformatics inference system design.
Arredondo, Tomás; Ormazábal, Wladimir
2015-01-01
This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.
Active Inference, homeostatic regulation and adaptive behavioural control.
Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl
2015-11-01
We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Bayesian inference and updating of reliability data
International Nuclear Information System (INIS)
Sabri, Z.A.; Cullingford, M.C.; David, H.T.; Husseiny, A.A.
1980-01-01
A Bayes methodology for inference of reliability values using available but scarce current data is discussed. The method can be used to update failure rates as more information becomes available from field experience, assuming that the performance of a given component (or system) exhibits a nonhomogeneous Poisson process. Bayes' theorem is used to summarize the historical evidence and current component data in the form of a posterior distribution suitable for prediction and for smoothing or interpolation. An example is given. It may be appropriate to apply the methodology developed here to human error data, in which case the exponential model might be used to describe the learning behavior of the operator or maintenance crew personnel
Automatic inference of indexing rules for MEDLINE
Directory of Open Access Journals (Sweden)
Shooshan Sonya E
2008-11-01
Full Text Available Abstract Background: Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. Methods: In this paper, we describe the use and the customization of Inductive Logic Programming (ILP to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Results: Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI, a system producing automatic indexing recommendations for MEDLINE. Conclusion: We expect the sets of ILP rules obtained in this experiment to be integrated into MTI.
Inferring Phylogenetic Networks from Gene Order Data
Directory of Open Access Journals (Sweden)
Alexey Anatolievich Morozov
2013-01-01
Full Text Available Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary, sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm. Binary encoding can also be useful, but only when the methods mentioned above cannot be used.
Supplier Selection Using Fuzzy Inference System
Directory of Open Access Journals (Sweden)
hamidreza kadhodazadeh
2014-01-01
Full Text Available Suppliers are one of the most vital parts of supply chain whose operation has significant indirect effect on customer satisfaction. Since customer's expectations from organization are different, organizations should consider different standards, respectively. There are many researches in this field using different standards and methods in recent years. The purpose of this study is to propose an approach for choosing a supplier in a food manufacturing company considering cost, quality, service, type of relationship and structure standards of the supplier organization. To evaluate supplier according to the above standards, the fuzzy inference system has been used. Input data of this system includes supplier's score in any standard that is achieved by AHP approach and the output is final score of each supplier. Finally, a supplier has been selected that although is not the best in price and quality, has achieved good score in all of the standards.
Gene expression inference with deep learning.
Chen, Yifei; Li, Yi; Narayan, Rajiv; Subramanian, Aravind; Xie, Xiaohui
2016-06-15
Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. D-GEX is available at https://github.com/uci-cbcl/D-GEX CONTACT: xhx@ics.uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Systematic parameter inference in stochastic mesoscopic modeling
Energy Technology Data Exchange (ETDEWEB)
Lei, Huan; Yang, Xiu [Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Li, Zhen [Division of Applied Mathematics, Brown University, Providence, RI 02912 (United States); Karniadakis, George Em, E-mail: george_karniadakis@brown.edu [Division of Applied Mathematics, Brown University, Providence, RI 02912 (United States)
2017-02-01
We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are “sparse”. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.
State-Space Inference and Learning with Gaussian Processes
Turner, R; Deisenroth, MP; Rasmussen, CE
2010-01-01
18.10.13 KB. Ok to add author version to spiral, authors hold copyright. State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. C...
Probabilistic logic networks a comprehensive framework for uncertain inference
Goertzel, Ben; Goertzel, Izabela Freire; Heljakka, Ari
2008-01-01
This comprehensive book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. A broad scope of reasoning types are considered.
Parametric statistical inference basic theory and modern approaches
Zacks, Shelemyahu; Tsokos, C P
1981-01-01
Parametric Statistical Inference: Basic Theory and Modern Approaches presents the developments and modern trends in statistical inference to students who do not have advanced mathematical and statistical preparation. The topics discussed in the book are basic and common to many fields of statistical inference and thus serve as a jumping board for in-depth study. The book is organized into eight chapters. Chapter 1 provides an overview of how the theory of statistical inference is presented in subsequent chapters. Chapter 2 briefly discusses statistical distributions and their properties. Chapt
Meeting cross-section requirements for nuclear-energy design
Energy Technology Data Exchange (ETDEWEB)
Weisbin, C.R.; de Saussure, G.; Santoro, R.T. (Oak Ridge National Lab., TN (USA)); Gilai, T. (Ben-Gurion Univ. of the Negev, Beersheba (Israel))
1982-01-01
Current requirements in cross-section data that are essential to nuclear-energy programmes are summarized and explained and some insight into how these data might be obtained is provided. The six sections of the paper describe: design parameters and target accuracies; data collection, evaluation and analysis; determination of high-accuracy differential nuclear data for technological applications; status of selected evaluated nuclear data; analysis of benchmark testing; identification of important cross sections and inferred needs.
Serang, Oliver
2014-01-01
Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called “causal independence”). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to and the space to where is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions. PMID:24626234
Making inference from wildlife collision data: inferring predator absence from prey strikes
Directory of Open Access Journals (Sweden)
Peter Caley
2017-02-01
Full Text Available Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.
Making inference from wildlife collision data: inferring predator absence from prey strikes.
Caley, Peter; Hosack, Geoffrey R; Barry, Simon C
2017-01-01
Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.
Inferred demand and supply elasticities from a comparison of world oil models
International Nuclear Information System (INIS)
Huntington, H.G.
1992-01-01
This paper summarizes the responses of oil supply and demand to prices and income in 11 world oil models that were compared in a recent Energy Modeling Forum (EMF) study. In May 1989, the EMF commenced a study of international oil supplies and demands (hereafter, EMF-11) to compare alternative perspectives on supply and demand issues and how these developments influence the level and direction of world oil prices. In analysing these issues, the EMF-11 working group relied partly upon results from 11 world oil models, using standardized assumptions about oil prices and gross domestic product (GDP). During the study, inferred price elasticities of supply and demand were derived from a comparison of results across different oil price scenarios with the same GDP growth path. Inferred income elasticities of demand were derived from a comparison of results across different economic growth scenarios with the same oil price-path. Together, these estimates summarize several important relationships for understanding oil markets. The first section provides some background on the EMF study and on general trends in the scenarios of interest that help to understand the results. Following sections explain the derivation and qualifications of the inferred estimates, report the results and summarize the key conclusions. (author)
Making Inferences in Adulthood: Falling Leaves Mean It's Fall.
Zandi, Taher; Gregory, Monica E.
1988-01-01
Assessed age differences in making inferences from prose. Older adults correctly answered mean of 10 questions related to implicit information and 8 related to explicit information. Young adults answered mean of 7 implicit and 12 explicit information questions. In spite of poorer recall of factual details, older subjects made inferences to greater…
Statistical Inference and Patterns of Inequality in the Global North
Moran, Timothy Patrick
2006-01-01
Cross-national inequality trends have historically been a crucial field of inquiry across the social sciences, and new methodological techniques of statistical inference have recently improved the ability to analyze these trends over time. This paper applies Monte Carlo, bootstrap inference methods to the income surveys of the Luxembourg Income…
Causal Effect Inference with Deep Latent-Variable Models
Louizos, C; Shalit, U.; Mooij, J.; Sontag, D.; Zemel, R.; Welling, M.
2017-01-01
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of
A Comparative Analysis of Fuzzy Inference Engines in Context of ...
African Journals Online (AJOL)
Fuzzy inference engine has found successful applications in a wide variety of fields, such as automatic control, data classification, decision analysis, expert engines, time series prediction, robotics, pattern recognition, etc. This paper presents a comparative analysis of three fuzzy inference engines, max-product, max-min ...
General Purpose Probabilistic Programming Platform with Effective Stochastic Inference
2018-04-01
REFERENCES 74 LIST OF ACRONYMS 80 ii List of Figures Figure 1. The problem of inferring curves from data while simultaneously choosing the...bottom path) as the inverse problem to computer graphics (top path). ........ 18 Figure 18. An illustration of generative probabilistic graphics for 3D...Building these systems involves simultaneously developing mathematical models, inference algorithms and optimized software implementations. Small changes
A Comparative Analysis of Fuzzy Inference Engines in Context of ...
African Journals Online (AJOL)
PROF. O. E. OSUAGWU
Fuzzy Inference engine is an important part of reasoning systems capable of extracting correct conclusions from ... is known as the inference, or rule definition portion, of fuzzy .... minimal set of decision rules based on input- ... The study uses Mamdani FIS model and. Sugeno FIS ... control of induction motor drive. [18] study.
Deontic Introduction: A Theory of Inference from Is to Ought
Elqayam, Shira; Thompson, Valerie A.; Wilkinson, Meredith R.; Evans, Jonathan St. B. T.; Over, David E.
2015-01-01
Humans have a unique ability to generate novel norms. Faced with the knowledge that there are hungry children in Somalia, we easily and naturally infer that we ought to donate to famine relief charities. Although a contentious and lively issue in metaethics, such inference from "is" to "ought" has not been systematically…
Causal inference in survival analysis using pseudo-observations
DEFF Research Database (Denmark)
Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T
2017-01-01
Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs ...
Bayesian inference of radiation belt loss timescales.
Camporeale, E.; Chandorkar, M.
2017-12-01
Electron fluxes in the Earth's radiation belts are routinely studied using the classical quasi-linear radial diffusion model. Although this simplified linear equation has proven to be an indispensable tool in understanding the dynamics of the radiation belt, it requires specification of quantities such as the diffusion coefficient and electron loss timescales that are never directly measured. Researchers have so far assumed a-priori parameterisations for radiation belt quantities and derived the best fit using satellite data. The state of the art in this domain lacks a coherent formulation of this problem in a probabilistic framework. We present some recent progress that we have made in performing Bayesian inference of radial diffusion parameters. We achieve this by making extensive use of the theory connecting Gaussian Processes and linear partial differential equations, and performing Markov Chain Monte Carlo sampling of radial diffusion parameters. These results are important for understanding the role and the propagation of uncertainties in radiation belt simulations and, eventually, for providing a probabilistic forecast of energetic electron fluxes in a Space Weather context.
Scalable inference for stochastic block models
Peng, Chengbin
2017-12-08
Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of "big data," traditional inference algorithms for such a model are increasingly limited due to their high time complexity and poor scalability. In this paper, we propose a multi-stage maximum likelihood approach to recover the latent parameters of the stochastic block model, in time linear with respect to the number of edges. We also propose a parallel algorithm based on message passing. Our algorithm can overlap communication and computation, providing speedup without compromising accuracy as the number of processors grows. For example, to process a real-world graph with about 1.3 million nodes and 10 million edges, our algorithm requires about 6 seconds on 64 cores of a contemporary commodity Linux cluster. Experiments demonstrate that the algorithm can produce high quality results on both benchmark and real-world graphs. An example of finding more meaningful communities is illustrated consequently in comparison with a popular modularity maximization algorithm.
Probabilistic learning and inference in schizophrenia
Averbeck, Bruno B.; Evans, Simon; Chouhan, Viraj; Bristow, Eleanor; Shergill, Sukhwinder S.
2010-01-01
Patients with schizophrenia make decisions on the basis of less evidence when required to collect information to make an inference, a behavior often called jumping to conclusions. The underlying basis for this behaviour remains controversial. We examined the cognitive processes underpinning this finding by testing subjects on the beads task, which has been used previously to elicit jumping to conclusions behaviour, and a stochastic sequence learning task, with a similar decision theoretic structure. During the sequence learning task, subjects had to learn a sequence of button presses, while receiving noisy feedback on their choices. We fit a Bayesian decision making model to the sequence task and compared model parameters to the choice behavior in the beads task in both patients and healthy subjects. We found that patients did show a jumping to conclusions style; and those who picked early in the beads task tended to learn less from positive feedback in the sequence task. This favours the likelihood of patients selecting early because they have a low threshold for making decisions, and that they make choices on the basis of relatively little evidence. PMID:20810252
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.
Aesthetic quality inference for online fashion shopping
Chen, Ming; Allebach, Jan
2014-03-01
On-line fashion communities in which participants post photos of personal fashion items for viewing and possible purchase by others are becoming increasingly popular. Generally, these photos are taken by individuals who have no training in photography with low-cost mobile phone cameras. It is desired that photos of the products have high aesthetic quality to improve the users' online shopping experience. In this work, we design features for aesthetic quality inference in the context of online fashion shopping. Psychophysical experiments are conducted to construct a database of the photos' aesthetic evaluation, specifically for photos from an online fashion shopping website. We then extract both generic low-level features and high-level image attributes to represent the aesthetic quality. Using a support vector machine framework, we train a predictor of the aesthetic quality rating based on the feature vector. Experimental results validate the efficacy of our approach. Metadata such as the product type are also used to further improve the result.
Information-Theoretic Inference of Common Ancestors
Directory of Open Access Journals (Sweden)
Bastian Steudel
2015-04-01
Full Text Available A directed acyclic graph (DAG partially represents the conditional independence structure among observations of a system if the local Markov condition holds, that is if every variable is independent of its non-descendants given its parents. In general, there is a whole class of DAGs that represents a given set of conditional independence relations. We are interested in properties of this class that can be derived from observations of a subsystem only. To this end, we prove an information-theoretic inequality that allows for the inference of common ancestors of observed parts in any DAG representing some unknown larger system. More explicitly, we show that a large amount of dependence in terms of mutual information among the observations implies the existence of a common ancestor that distributes this information. Within the causal interpretation of DAGs, our result can be seen as a quantitative extension of Reichenbach’s principle of common cause to more than two variables. Our conclusions are valid also for non-probabilistic observations, such as binary strings, since we state the proof for an axiomatized notion of “mutual information” that includes the stochastic as well as the algorithmic version.
Probabilistic learning and inference in schizophrenia.
Averbeck, Bruno B; Evans, Simon; Chouhan, Viraj; Bristow, Eleanor; Shergill, Sukhwinder S
2011-04-01
Patients with schizophrenia make decisions on the basis of less evidence when required to collect information to make an inference, a behavior often called jumping to conclusions. The underlying basis for this behavior remains controversial. We examined the cognitive processes underpinning this finding by testing subjects on the beads task, which has been used previously to elicit jumping to conclusions behavior, and a stochastic sequence learning task, with a similar decision theoretic structure. During the sequence learning task, subjects had to learn a sequence of button presses, while receiving a noisy feedback on their choices. We fit a Bayesian decision making model to the sequence task and compared model parameters to the choice behavior in the beads task in both patients and healthy subjects. We found that patients did show a jumping to conclusions style; and those who picked early in the beads task tended to learn less from positive feedback in the sequence task. This favours the likelihood of patients selecting early because they have a low threshold for making decisions, and that they make choices on the basis of relatively little evidence. Published by Elsevier B.V.
Active Inference and Learning in the Cerebellum.
Friston, Karl; Herreros, Ivan
2016-09-01
This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry-and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception.
Inferring gene networks from discrete expression data
Zhang, L.
2013-07-18
The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNAtranscripts in cell samples.We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution.We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference. © The Author 2013. Published by Oxford University Press. All rights reserved.
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
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.
Logical inference techniques for loop parallelization
Oancea, Cosmin E.; Rauchwerger, Lawrence
2012-01-01
This paper presents a fully automatic approach to loop parallelization that integrates the use of static and run-time analysis and thus overcomes many known difficulties such as nonlinear and indirect array indexing and complex control flow. Our hybrid analysis framework validates the parallelization transformation by verifying the independence of the loop's memory references. To this end it represents array references using the USR (uniform set representation) language and expresses the independence condition as an equation, S = Ø, where S is a set expression representing array indexes. Using a language instead of an array-abstraction representation for S results in a smaller number of conservative approximations but exhibits a potentially-high runtime cost. To alleviate this cost we introduce a language translation F from the USR set-expression language to an equally rich language of predicates (F(S) ⇒ S = Ø). Loop parallelization is then validated using a novel logic inference algorithm that factorizes the obtained complex predicates (F(S)) into a sequence of sufficient-independence conditions that are evaluated first statically and, when needed, dynamically, in increasing order of their estimated complexities. We evaluate our automated solution on 26 benchmarks from PERFECTCLUB and SPEC suites and show that our approach is effective in parallelizing large, complex loops and obtains much better full program speedups than the Intel and IBM Fortran compilers. Copyright © 2012 ACM.
BAYESIAN INFERENCE OF CMB GRAVITATIONAL LENSING
Energy Technology Data Exchange (ETDEWEB)
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.
Virtual reality and consciousness inference in dreaming.
Hobson, J Allan; Hong, Charles C-H; Friston, Karl J
2014-01-01
This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that - through experience-dependent plasticity - becomes a generative or predictive model of the world. This model, which is most clearly revealed in rapid eye movement (REM) sleep dreaming, may provide the theater for conscious experience. Functional neuroimaging evidence for brain activations that are time-locked to rapid eye movements (REMs) endorses the view that waking consciousness emerges from REM sleep - and dreaming lays the foundations for waking perception. In this view, the brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming. In contrast, dreaming plays an essential role in maintaining and enhancing the capacity to model the world by minimizing model complexity and thereby maximizing both statistical and thermodynamic efficiency. This perspective suggests that consciousness corresponds to the embodied process of inference, realized through the generation of virtual realities (in both sleep and wakefulness). In short, our premise or hypothesis is that the waking brain engages with the world to predict the causes of sensations, while in sleep the brain's generative model is actively refined so that it generates more efficient predictions during waking. We review the evidence in support of this hypothesis - evidence that grounds consciousness in biophysical computations whose neuronal and neurochemical infrastructure has been disclosed by sleep research.
Inferring human mobility using communication patterns
Palchykov, Vasyl; Mitrović, Marija; Jo, Hang-Hyun; Saramäki, Jari; Pan, Raj Kumar
2014-08-01
Understanding the patterns of mobility of individuals is crucial for a number of reasons, from city planning to disaster management. There are two common ways of quantifying the amount of travel between locations: by direct observations that often involve privacy issues, e.g., tracking mobile phone locations, or by estimations from models. Typically, such models build on accurate knowledge of the population size at each location. However, when this information is not readily available, their applicability is rather limited. As mobile phones are ubiquitous, our aim is to investigate if mobility patterns can be inferred from aggregated mobile phone call data alone. Using data released by Orange for Ivory Coast, we show that human mobility is well predicted by a simple model based on the frequency of mobile phone calls between two locations and their geographical distance. We argue that the strength of the model comes from directly incorporating the social dimension of mobility. Furthermore, as only aggregated call data is required, the model helps to avoid potential privacy problems.
Inference-based procedural modeling of solids
Biggers, Keith
2011-11-01
As virtual environments become larger and more complex, there is an increasing need for more automated construction algorithms to support the development process. We present an approach for modeling solids by combining prior examples with a simple sketch. Our algorithm uses an inference-based approach to incrementally fit patches together in a consistent fashion to define the boundary of an object. This algorithm samples and extracts surface patches from input models, and develops a Petri net structure that describes the relationship between patches along an imposed parameterization. Then, given a new parameterized line or curve, we use the Petri net to logically fit patches together in a manner consistent with the input model. This allows us to easily construct objects of varying sizes and configurations using arbitrary articulation, repetition, and interchanging of parts. The result of our process is a solid model representation of the constructed object that can be integrated into a simulation-based environment. © 2011 Elsevier Ltd. All rights reserved.
Multiple sequence alignment accuracy and phylogenetic inference.
Ogden, T Heath; Rosenberg, Michael S
2006-04-01
Phylogenies are often thought to be more dependent upon the specifics of the sequence alignment rather than on the method of reconstruction. Simulation of sequences containing insertion and deletion events was performed in order to determine the role that alignment accuracy plays during phylogenetic inference. Data sets were simulated for pectinate, balanced, and random tree shapes under different conditions (ultrametric equal branch length, ultrametric random branch length, nonultrametric random branch length). Comparisons between hypothesized alignments and true alignments enabled determination of two measures of alignment accuracy, that of the total data set and that of individual branches. In general, our results indicate that as alignment error increases, topological accuracy decreases. This trend was much more pronounced for data sets derived from more pectinate topologies. In contrast, for balanced, ultrametric, equal branch length tree shapes, alignment inaccuracy had little average effect on tree reconstruction. These conclusions are based on average trends of many analyses under different conditions, and any one specific analysis, independent of the alignment accuracy, may recover very accurate or inaccurate topologies. Maximum likelihood and Bayesian, in general, outperformed neighbor joining and maximum parsimony in terms of tree reconstruction accuracy. Results also indicated that as the length of the branch and of the neighboring branches increase, alignment accuracy decreases, and the length of the neighboring branches is the major factor in topological accuracy. Thus, multiple-sequence alignment can be an important factor in downstream effects on topological reconstruction.
Phylogenetic inference with weighted codon evolutionary distances.
Criscuolo, Alexis; Michel, Christian J
2009-04-01
We develop a new approach to estimate a matrix of pairwise evolutionary distances from a codon-based alignment based on a codon evolutionary model. The method first computes a standard distance matrix for each of the three codon positions. Then these three distance matrices are weighted according to an estimate of the global evolutionary rate of each codon position and averaged into a unique distance matrix. Using a large set of both real and simulated codon-based alignments of nucleotide sequences, we show that this approach leads to distance matrices that have a significantly better treelikeness compared to those obtained by standard nucleotide evolutionary distances. We also propose an alternative weighting to eliminate the part of the noise often associated with some codon positions, particularly the third position, which is known to induce a fast evolutionary rate. Simulation results show that fast distance-based tree reconstruction algorithms on distance matrices based on this codon position weighting can lead to phylogenetic trees that are at least as accurate as, if not better, than those inferred by maximum likelihood. Finally, a well-known multigene dataset composed of eight yeast species and 106 codon-based alignments is reanalyzed and shows that our codon evolutionary distances allow building a phylogenetic tree which is similar to those obtained by non-distance-based methods (e.g., maximum parsimony and maximum likelihood) and also significantly improved compared to standard nucleotide evolutionary distance estimates.
Primate diversification inferred from phylogenies and fossils.
Herrera, James P
2017-12-01
Biodiversity arises from the balance between speciation and extinction. Fossils record the origins and disappearance of organisms, and the branching patterns of molecular phylogenies allow estimation of speciation and extinction rates, but the patterns of diversification are frequently incongruent between these two data sources. I tested two hypotheses about the diversification of primates based on ∼600 fossil species and 90% complete phylogenies of living species: (1) diversification rates increased through time; (2) a significant extinction event occurred in the Oligocene. Consistent with the first hypothesis, analyses of phylogenies supported increasing speciation rates and negligible extinction rates. In contrast, fossils showed that while speciation rates increased, speciation and extinction rates tended to be nearly equal, resulting in zero net diversification. Partially supporting the second hypothesis, the fossil data recorded a clear pattern of diversity decline in the Oligocene, although diversification rates were near zero. The phylogeny supported increased extinction ∼34 Ma, but also elevated extinction ∼10 Ma, coinciding with diversity declines in some fossil clades. The results demonstrated that estimates of speciation and extinction ignoring fossils are insufficient to infer diversification and information on extinct lineages should be incorporated into phylogenetic analyses. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.
Logical inference techniques for loop parallelization
Oancea, Cosmin E.
2012-01-01
This paper presents a fully automatic approach to loop parallelization that integrates the use of static and run-time analysis and thus overcomes many known difficulties such as nonlinear and indirect array indexing and complex control flow. Our hybrid analysis framework validates the parallelization transformation by verifying the independence of the loop\\'s memory references. To this end it represents array references using the USR (uniform set representation) language and expresses the independence condition as an equation, S = Ø, where S is a set expression representing array indexes. Using a language instead of an array-abstraction representation for S results in a smaller number of conservative approximations but exhibits a potentially-high runtime cost. To alleviate this cost we introduce a language translation F from the USR set-expression language to an equally rich language of predicates (F(S) ⇒ S = Ø). Loop parallelization is then validated using a novel logic inference algorithm that factorizes the obtained complex predicates (F(S)) into a sequence of sufficient-independence conditions that are evaluated first statically and, when needed, dynamically, in increasing order of their estimated complexities. We evaluate our automated solution on 26 benchmarks from PERFECTCLUB and SPEC suites and show that our approach is effective in parallelizing large, complex loops and obtains much better full program speedups than the Intel and IBM Fortran compilers. Copyright © 2012 ACM.
Inferring Molecular Processes Heterogeneity from Transcriptional Data.
Gogolewski, Krzysztof; Wronowska, Weronika; Lech, Agnieszka; Lesyng, Bogdan; Gambin, Anna
2017-01-01
RNA microarrays and RNA-seq are nowadays standard technologies to study the transcriptional activity of cells. Most studies focus on tracking transcriptional changes caused by specific experimental conditions. Information referring to genes up- and downregulation is evaluated analyzing the behaviour of relatively large population of cells by averaging its properties. However, even assuming perfect sample homogeneity, different subpopulations of cells can exhibit diverse transcriptomic profiles, as they may follow different regulatory/signaling pathways. The purpose of this study is to provide a novel methodological scheme to account for possible internal, functional heterogeneity in homogeneous cell lines, including cancer ones. We propose a novel computational method to infer the proportion between subpopulations of cells that manifest various functional behaviour in a given sample. Our method was validated using two datasets from RNA microarray experiments. Both experiments aimed to examine cell viability in specific experimental conditions. The presented methodology can be easily extended to RNA-seq data as well as other molecular processes. Moreover, it complements standard tools to indicate most important networks from transcriptomic data and in particular could be useful in the analysis of cancer cell lines affected by biologically active compounds or drugs.
Contingency inferences driven by base rates: Valid by sampling
Directory of Open Access Journals (Sweden)
Florian Kutzner
2011-04-01
Full Text Available Fiedler et al. (2009, reviewed evidence for the utilization of a contingency inference strategy termed pseudocontingencies (PCs. In PCs, the more frequent levels (and, by implication, the less frequent levels are assumed to be associated. PCs have been obtained using a wide range of task settings and dependent measures. Yet, the readiness with which decision makers rely on PCs is poorly understood. A computer simulation explored two potential sources of subjective validity of PCs. First, PCs are shown to perform above chance level when the task is to infer the sign of moderate to strong population contingencies from a sample of observations. Second, contingency inferences based on PCs and inferences based on cell frequencies are shown to partially agree across samples. Intriguingly, this criterion and convergent validity are by-products of random sampling error, highlighting the inductive nature of contingency inferences.
Quantum-Like Representation of Non-Bayesian Inference
Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.
2013-01-01
This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.
Statistical causal inferences and their applications in public health research
Wu, Pan; Chen, Ding-Geng
2016-01-01
This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.
Human Inferences about Sequences: A Minimal Transition Probability Model.
Directory of Open Access Journals (Sweden)
Florent Meyniel
2016-12-01
Full Text Available The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.
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...
Inferring climate variability from skewed proxy records
Emile-Geay, J.; Tingley, M.
2013-12-01
Many paleoclimate analyses assume a linear relationship between the proxy and the target climate variable, and that both the climate quantity and the errors follow normal distributions. An ever-increasing number of proxy records, however, are better modeled using distributions that are heavy-tailed, skewed, or otherwise non-normal, on account of the proxies reflecting non-normally distributed climate variables, or having non-linear relationships with a normally distributed climate variable. The analysis of such proxies requires a different set of tools, and this work serves as a cautionary tale on the danger of making conclusions about the underlying climate from applications of classic statistical procedures to heavily skewed proxy records. Inspired by runoff proxies, we consider an idealized proxy characterized by a nonlinear, thresholded relationship with climate, and describe three approaches to using such a record to infer past climate: (i) applying standard methods commonly used in the paleoclimate literature, without considering the non-linearities inherent to the proxy record; (ii) applying a power transform prior to using these standard methods; (iii) constructing a Bayesian model to invert the mechanistic relationship between the climate and the proxy. We find that neglecting the skewness in the proxy leads to erroneous conclusions and often exaggerates changes in climate variability between different time intervals. In contrast, an explicit treatment of the skewness, using either power transforms or a Bayesian inversion of the mechanistic model for the proxy, yields significantly better estimates of past climate variations. We apply these insights in two paleoclimate settings: (1) a classical sedimentary record from Laguna Pallcacocha, Ecuador (Moy et al., 2002). Our results agree with the qualitative aspects of previous analyses of this record, but quantitative departures are evident and hold implications for how such records are interpreted, and
Vertically Integrated Seismological Analysis II : Inference
Arora, N. S.; Russell, S.; Sudderth, E.
2009-12-01
Methods for automatically associating detected waveform features with hypothesized seismic events, and localizing those events, are a critical component of efforts to verify the Comprehensive Test Ban Treaty (CTBT). As outlined in our companion abstract, we have developed a hierarchical model which views detection, association, and localization as an integrated probabilistic inference problem. In this abstract, we provide more details on the Markov chain Monte Carlo (MCMC) methods used to solve this inference task. MCMC generates samples from a posterior distribution π(x) over possible worlds x by defining a Markov chain whose states are the worlds x, and whose stationary distribution is π(x). In the Metropolis-Hastings (M-H) method, transitions in the Markov chain are constructed in two steps. First, given the current state x, a candidate next state x‧ is generated from a proposal distribution q(x‧ | x), which may be (more or less) arbitrary. Second, the transition to x‧ is not automatic, but occurs with an acceptance probability—α(x‧ | x) = min(1, π(x‧)q(x | x‧)/π(x)q(x‧ | x)). The seismic event model outlined in our companion abstract is quite similar to those used in multitarget tracking, for which MCMC has proved very effective. In this model, each world x is defined by a collection of events, a list of properties characterizing those events (times, locations, magnitudes, and types), and the association of each event to a set of observed detections. The target distribution π(x) = P(x | y), the posterior distribution over worlds x given the observed waveform data y at all stations. Proposal distributions then implement several types of moves between worlds. For example, birth moves create new events; death moves delete existing events; split moves partition the detections for an event into two new events; merge moves combine event pairs; swap moves modify the properties and assocations for pairs of events. Importantly, the rules for
Network inference via adaptive optimal design
Directory of Open Access Journals (Sweden)
Stigter Johannes D
2012-09-01
Full Text Available Abstract Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design. Findings The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure. Conclusion We conclude that a careful experiment design very often (but not always pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.
On the Hardness of Topology Inference
Acharya, H. B.; Gouda, M. G.
Many systems require information about the topology of networks on the Internet, for purposes like management, efficiency, testing of new protocols and so on. However, ISPs usually do not share the actual topology maps with outsiders; thus, in order to obtain the topology of a network on the Internet, a system must reconstruct it from publicly observable data. The standard method employs traceroute to obtain paths between nodes; next, a topology is generated such that the observed paths occur in the graph. However, traceroute has the problem that some routers refuse to reveal their addresses, and appear as anonymous nodes in traces. Previous research on the problem of topology inference with anonymous nodes has demonstrated that it is at best NP-complete. In this paper, we improve upon this result. In our previous research, we showed that in the special case where nodes may be anonymous in some traces but not in all traces (so all node identifiers are known), there exist trace sets that are generable from multiple topologies. This paper extends our theory of network tracing to the general case (with strictly anonymous nodes), and shows that the problem of computing the network that generated a trace set, given the trace set, has no general solution. The weak version of the problem, which allows an algorithm to output a "small" set of networks- any one of which is the correct one- is also not solvable. Any algorithm guaranteed to output the correct topology outputs at least an exponential number of networks. Our results are surprisingly robust: they hold even when the network is known to have exactly two anonymous nodes, and every node as well as every edge in the network is guaranteed to occur in some trace. On the basis of this result, we suggest that exact reconstruction of network topology requires more powerful tools than traceroute.
Statistical Inference for Data Adaptive Target Parameters.
Hubbard, Alan E; Kherad-Pajouh, Sara; van der Laan, Mark J
2016-05-01
Consider one observes n i.i.d. copies of a random variable with a probability distribution that is known to be an element of a particular statistical model. In order to define our statistical target we partition the sample in V equal size sub-samples, and use this partitioning to define V splits in an estimation sample (one of the V subsamples) and corresponding complementary parameter-generating sample. For each of the V parameter-generating samples, we apply an algorithm that maps the sample to a statistical target parameter. We define our sample-split data adaptive statistical target parameter as the average of these V-sample specific target parameters. We present an estimator (and corresponding central limit theorem) of this type of data adaptive target parameter. This general methodology for generating data adaptive target parameters is demonstrated with a number of practical examples that highlight new opportunities for statistical learning from data. This new framework provides a rigorous statistical methodology for both exploratory and confirmatory analysis within the same data. Given that more research is becoming "data-driven", the theory developed within this paper provides a new impetus for a greater involvement of statistical inference into problems that are being increasingly addressed by clever, yet ad hoc pattern finding methods. To suggest such potential, and to verify the predictions of the theory, extensive simulation studies, along with a data analysis based on adaptively determined intervention rules are shown and give insight into how to structure such an approach. The results show that the data adaptive target parameter approach provides a general framework and resulting methodology for data-driven science.
Inferring modules from human protein interactome classes
Directory of Open Access Journals (Sweden)
Chaurasia Gautam
2010-07-01
Full Text Available Abstract Background The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features. Results We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated data sources, which we call interactome classes. Due to class diversity, we deal with variable dependencies of data features arising from structural specificities and biases, but also from possible overlaps. Since highly connected regions of the human interactome may point to potential protein complexes, we have focused on the concept of modularity, and elucidated the detection power of module extraction algorithms by independent validations based on GO, MIPS and KEGG. From the combination of protein interactions with gene expressions, a confidence scoring scheme has been proposed before proceeding via GO with further classification in permanent and transient modules. Conclusions Disaggregated interactomes are shown to be informative for inferring modularity, thus contributing to perform an effective integrative analysis. Validation of the extracted modules by multiple annotation allows for the assessment of confidence measures assigned to the modules in a protein pathway context. Notably, the proposed multilayer confidence scheme can be used for network calibration by enabling a transition from unweighted to weighted interactomes based on biological evidence.
Ensemble stacking mitigates biases in inference of synaptic connectivity.
Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N
2018-01-01
A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.
Causal inference in biology networks with integrated belief propagation.
Chang, Rui; Karr, Jonathan R; Schadt, Eric E
2015-01-01
Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.
Tarlowski, Andrzej
2018-01-01
There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects.
Directory of Open Access Journals (Sweden)
Andrzej Tarlowski
2018-04-01
Full Text Available There is a lively debate concerning the role of conceptual and perceptual information in young children's inductive inferences. While most studies focus on the role of basic level categories in induction the present research contributes to the debate by asking whether children's inductions are guided by ontological constraints. Two studies use a novel inductive paradigm to test whether young children have an expectation that all animals share internal commonalities that do not extend to perceptually similar inanimates. The results show that children make category-consistent responses when asked to project an internal feature from an animal to either a dissimilar animal or a similar toy replica. However, the children do not have a universal preference for category-consistent responses in an analogous task involving vehicles and vehicle toy replicas. The results also show the role of context and individual factors in inferences. Children's early reliance on ontological commitments in induction cannot be explained by perceptual similarity or by children's sensitivity to the authenticity of objects.
Bayesian inference of substrate properties from film behavior
International Nuclear Information System (INIS)
Aggarwal, R; Demkowicz, M J; Marzouk, Y M
2015-01-01
We demonstrate that by observing the behavior of a film deposited on a substrate, certain features of the substrate may be inferred with quantified uncertainty using Bayesian methods. We carry out this demonstration on an illustrative film/substrate model where the substrate is a Gaussian random field and the film is a two-component mixture that obeys the Cahn–Hilliard equation. We construct a stochastic reduced order model to describe the film/substrate interaction and use it to infer substrate properties from film behavior. This quantitative inference strategy may be adapted to other film/substrate systems. (paper)
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...
Working memory supports inference learning just like classification learning.
Craig, Stewart; Lewandowsky, Stephan
2013-08-01
Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.
Surrogate based approaches to parameter inference in ocean models
Knio, Omar
2016-01-06
This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.
Ensemble stacking mitigates biases in inference of synaptic connectivity
Directory of Open Access Journals (Sweden)
Brendan Chambers
2018-03-01
Full Text Available A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures
Brain Imaging, Forward Inference, and Theories of Reasoning
Heit, Evan
2015-01-01
This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities. PMID:25620926
Fast and scalable inference of multi-sample cancer lineages.
Popic, Victoria
2015-05-06
Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .
Fast and scalable inference of multi-sample cancer lineages.
Popic, Victoria; Salari, Raheleh; Hajirasouliha, Iman; Kashef-Haghighi, Dorna; West, Robert B; Batzoglou, Serafim
2015-01-01
Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .
International Conference on Trends and Perspectives in Linear Statistical Inference
Rosen, Dietrich
2018-01-01
This volume features selected contributions on a variety of topics related to linear statistical inference. The peer-reviewed papers from the International Conference on Trends and Perspectives in Linear Statistical Inference (LinStat 2016) held in Istanbul, Turkey, 22-25 August 2016, cover topics in both theoretical and applied statistics, such as linear models, high-dimensional statistics, computational statistics, the design of experiments, and multivariate analysis. The book is intended for statisticians, Ph.D. students, and professionals who are interested in statistical inference. .
Brain imaging, forward inference, and theories of reasoning.
Heit, Evan
2014-01-01
This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities.
Surrogate based approaches to parameter inference in ocean models
Knio, Omar
2016-01-01
This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.
Models and Inference for Multivariate Spatial Extremes
Vettori, Sabrina
2017-12-07
The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical
Conditions for Confirmatory Analysis and Causal Inference.
1982-05-03
such behavior evolved because it had survival value. Other philosophers have not been so kind in dealing with Hume’s skepticism. Wittgenstein regarded...also in many other ways." _ Ludwig Wittgenstein (1980) We begin this section by restating the third principle for the logical foundation of...study in Wittgenstein . Oxford: Basil Blackwell, 1973. Dubin, R. Theory building in applied areas. In M. D. Dunnette (Ed.), Handbook of industrial and
García, Miguel A; Costea, Mihai; Kuzmina, Maria; Stefanović, Saša
2014-04-01
The parasitic genus Cuscuta, containing some 200 species circumscribed traditionally in three subgenera, is nearly cosmopolitan, occurring in a wide range of habitats and hosts. Previous molecular studies, on subgenera Grammica and Cuscuta, delimited major clades within these groups. However, the sequences used were unalignable among subgenera, preventing the phylogenetic comparison across the genus. We conducted a broad phylogenetic study using rbcL and nrLSU sequences covering the morphological, physiological, and geographical diversity of Cuscuta. We used parsimony methods to reconstruct ancestral states for taxonomically important characters. Biogeographical inferences were obtained using statistical and Bayesian approaches. Four well-supported major clades are resolved. Two of them correspond to subgenera Monogynella and Grammica. Subgenus Cuscuta is paraphyletic, with section Pachystigma sister to subgenus Grammica. Previously described cases of strongly supported discordance between plastid and nuclear phylogenies, interpreted as reticulation events, are confirmed here and three new cases are detected. Dehiscent fruits and globose stigmas are inferred as ancestral character states, whereas the ancestral style number is ambiguous. Biogeographical reconstructions suggest an Old World origin for the genus and subsequent spread to the Americas as a consequence of one long-distance dispersal. Hybridization may play an important yet underestimated role in the evolution of Cuscuta. Our results disagree with scenarios of evolution (polarity) previously proposed for several taxonomically important morphological characters, and with their usage and significance. While several cases of long-distance dispersal are inferred, vicariance or dispersal to adjacent areas emerges as the dominant biogeographical pattern.
Indirect Inference for Stochastic Differential Equations Based on Moment Expansions
Ballesio, Marco; Tempone, Raul; Vilanova, Pedro
2016-01-01
We provide an indirect inference method to estimate the parameters of timehomogeneous scalar diffusion and jump diffusion processes. We obtain a system of ODEs that approximate the time evolution of the first two moments of the process
ESPRIT: Exercise Sensing and Pose Recovery Inference Tool, Phase I
National Aeronautics and Space Administration — We propose to develop ESPRIT: an Exercise Sensing and Pose Recovery Inference Tool, in support of NASA's effort in developing crew exercise technologies for...
Automated Flight Safety Inference Engine (AFSIE) System, Phase I
National Aeronautics and Space Administration — We propose to develop an innovative Autonomous Flight Safety Inference Engine (AFSIE) system to autonomously and reliably terminate the flight of an errant launch...
Classification versus inference learning contrasted with real-world categories.
Jones, Erin L; Ross, Brian H
2011-07-01
Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.
Efficient Exact Inference With Loss Augmented Objective in Structured Learning.
Bauer, Alexander; Nakajima, Shinichi; Muller, Klaus-Robert
2016-08-19
Structural support vector machine (SVM) is an elegant approach for building complex and accurate models with structured outputs. However, its applicability relies on the availability of efficient inference algorithms--the state-of-the-art training algorithms repeatedly perform inference to compute a subgradient or to find the most violating configuration. In this paper, we propose an exact inference algorithm for maximizing nondecomposable objectives due to special type of a high-order potential having a decomposable internal structure. As an important application, our method covers the loss augmented inference, which enables the slack and margin scaling formulations of structural SVM with a variety of dissimilarity measures, e.g., Hamming loss, precision and recall, Fβ-loss, intersection over union, and many other functions that can be efficiently computed from the contingency table. We demonstrate the advantages of our approach in natural language parsing and sequence segmentation applications.
BagReg: Protein inference through machine learning.
Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou
2015-08-01
Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.
The Human Cochlear Mechanical Nonlinearity Inferred via Psychometric Functions
Directory of Open Access Journals (Sweden)
Nizami Lance
2013-12-01
Extension of the model of Schairer and colleagues results in credible cochlear nonlinearities in man, suggesting that forward-masking provides a non-invasive way to infer the human mechanical cochlear nonlinearity.
A general Bayes weibull inference model for accelerated life testing
International Nuclear Information System (INIS)
Dorp, J. Rene van; Mazzuchi, Thomas A.
2005-01-01
This article presents the development of a general Bayes inference model for accelerated life testing. The failure times at a constant stress level are assumed to belong to a Weibull distribution, but the specification of strict adherence to a parametric time-transformation function is not required. Rather, prior information is used to indirectly define a multivariate prior distribution for the scale parameters at the various stress levels and the common shape parameter. Using the approach, Bayes point estimates as well as probability statements for use-stress (and accelerated) life parameters may be inferred from a host of testing scenarios. The inference procedure accommodates both the interval data sampling strategy and type I censored sampling strategy for the collection of ALT test data. The inference procedure uses the well-known MCMC (Markov Chain Monte Carlo) methods to derive posterior approximations. The approach is illustrated with an example
Inference method using bayesian network for diagnosis of pulmonary nodules
International Nuclear Information System (INIS)
Kawagishi, Masami; Iizuka, Yoshio; Yamamoto, Hiroyuki; Yakami, Masahiro; Kubo, Takeshi; Fujimoto, Koji; Togashi, Kaori
2010-01-01
This report describes the improvements of a naive Bayes model that infers the diagnosis of pulmonary nodules in chest CT images based on the findings obtained when a radiologist interprets the CT images. We have previously introduced an inference model using a naive Bayes classifier and have reported its clinical value based on evaluation using clinical data. In the present report, we introduce the following improvements to the original inference model: the selection of findings based on correlations and the generation of a model using only these findings, and the introduction of classifiers that integrate several simple classifiers each of which is specialized for specific diagnosis. These improvements were found to increase the inference accuracy by 10.4% (p<.01) as compared to the original model in 100 cases (222 nodules) based on leave-one-out evaluation. (author)
Bayesian inference of chemical kinetic models from proposed reactions
Galagali, Nikhil; Marzouk, Youssef M.
2015-01-01
© 2014 Elsevier Ltd. Bayesian inference provides a natural framework for combining experimental data with prior knowledge to develop chemical kinetic models and quantify the associated uncertainties, not only in parameter values but also in model
Inference of beliefs and emotions in patients with Alzheimer's disease.
Zaitchik, Deborah; Koff, Elissa; Brownell, Hiram; Winner, Ellen; Albert, Marilyn
2006-01-01
The present study compared 20 patients with mild to moderate Alzheimer's disease with 20 older controls (ages 69-94 years) on their ability to make inferences about emotions and beliefs in others. Six tasks tested their ability to make 1st-order and 2nd-order inferences as well as to offer explanations and moral evaluations of human action by appeal to emotions and beliefs. Results showed that the ability to infer emotions and beliefs in 1st-order tasks remains largely intact in patients with mild to moderate Alzheimer's. Patients were able to use mental states in the prediction, explanation, and moral evaluation of behavior. Impairment on 2nd-order tasks involving inference of mental states was equivalent to impairment on control tasks, suggesting that patients' difficulty is secondary to their cognitive impairments. ((c) 2006 APA, all rights reserved).
Efficient design and inference in distributed Bayesian networks: an overview
de Oude, P.; Groen, F.C.A.; Pavlin, G.; Bezhanishvili, N.; Löbner, S.; Schwabe, K.; Spada, L.
2011-01-01
This paper discusses an approach to distributed Bayesian modeling and inference, which is relevant for an important class of contemporary real world situation assessment applications. By explicitly considering the locality of causal relations, the presented approach (i) supports coherent distributed
SDG multiple fault diagnosis by real-time inverse inference
International Nuclear Information System (INIS)
Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng
2005-01-01
In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency
SDG multiple fault diagnosis by real-time inverse inference
Energy Technology Data Exchange (ETDEWEB)
Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng
2005-02-01
In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency.
Bayesian Information Criterion as an Alternative way of Statistical Inference
Directory of Open Access Journals (Sweden)
Nadejda Yu. Gubanova
2012-05-01
Full Text Available The article treats Bayesian information criterion as an alternative to traditional methods of statistical inference, based on NHST. The comparison of ANOVA and BIC results for psychological experiment is discussed.
Statistical inferences for bearings life using sudden death test
Directory of Open Access Journals (Sweden)
Morariu Cristin-Olimpiu
2017-01-01
Full Text Available In this paper we propose a calculus method for reliability indicators estimation and a complete statistical inferences for three parameters Weibull distribution of bearings life. Using experimental values regarding the durability of bearings tested on stands by the sudden death tests involves a series of particularities of the estimation using maximum likelihood method and statistical inference accomplishment. The paper detailing these features and also provides an example calculation.
Inference in {open_quotes}poor{close_quotes} languages
Energy Technology Data Exchange (ETDEWEB)
Petrov, S. [Oak Ridge National Lab., TN (United States)
1996-12-31
Languages with a solvable implication problem but without complete and consistent systems of inference rules ({open_quote}poor{close_quote} languages) are considered. The problem of existence of a finite, complete, and consistent inference rule system for a {open_quotes}poor{close_quotes} language is stated independently of the language or the rule syntax. Several properties of the problem are proved. An application of the results to the language of join dependencies is given.
Inference of a Nonlinear Stochastic Model of the Cardiorespiratory Interaction
Smelyanskiy, V. N.; Luchinsky, D. G.; Stefanovska, A.; McClintock, P. V.
2005-03-01
We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set of polynomial basis functions representing the nonlinear force governing system oscillations. The strength and direction of coupling and noise intensity are simultaneously inferred from a univariate blood pressure signal. Our new inference technique does not require extensive global optimization, and it is applicable to a wide range of complex dynamical systems subject to noise.
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...
Completion is an Instance of Abstract Canonical System Inference
Burel , Guillaume; Kirchner , Claude
2006-01-01
http://www.springerlink.com/content/u222753gl333221p/; Abstract canonical systems and inference (ACSI) were introduced to formalize the intuitive notions of good proof and good inference appearing typically in first-order logic or in Knuth-Bendix like completion procedures. Since this abstract framework is intended to be generic, it is of fundamental interest to show its adequacy to represent the main systems of interest. This has been done for ground completion (where all equational axioms a...
Geostatistical inference using crosshole ground-penetrating radar
DEFF Research Database (Denmark)
Looms, Majken C; Hansen, Thomas Mejer; Cordua, Knud Skou
2010-01-01
of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance...... reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisturecontent measurements, obtained gravimetrically from samples collected at the field site....
Fused Regression for Multi-source Gene Regulatory Network Inference.
Directory of Open Access Journals (Sweden)
Kari Y Lam
2016-12-01
Full Text Available Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.
Bayesian Inference and Online Learning in Poisson Neuronal Networks.
Huang, Yanping; Rao, Rajesh P N
2016-08-01
Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.
Automatic physical inference with information maximizing neural networks
Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.
2018-04-01
Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.
Directory of Open Access Journals (Sweden)
Matsen Frederick A
2012-05-01
Full Text Available Abstract Background Although taxonomy is often used informally to evaluate the results of phylogenetic inference and the root of phylogenetic trees, algorithmic methods to do so are lacking. Results In this paper we formalize these procedures and develop algorithms to solve the relevant problems. In particular, we introduce a new algorithm that solves a "subcoloring" problem to express the difference between a taxonomy and a phylogeny at a given rank. This algorithm improves upon the current best algorithm in terms of asymptotic complexity for the parameter regime of interest; we also describe a branch-and-bound algorithm that saves orders of magnitude in computation on real data sets. We also develop a formalism and an algorithm for rooting phylogenetic trees according to a taxonomy. Conclusions The algorithms in this paper, and the associated freely-available software, will help biologists better use and understand taxonomically labeled phylogenetic trees.
Inferences of the deep solar meridional flow
Böning, Vincent G. A.
2017-10-01
Understanding the solar meridional flow is important for uncovering the origin of the solar activity cycle. Yet, recent helioseismic estimates of this flow have come to conflicting conclusions in deeper layers of the solar interior, i.e., at depths below about 0.9 solar radii. The aim of this thesis is to contribute to a better understanding of the deep solar meridional flow. Time-distance helioseismology is the major method for investigating this flow. In this method, travel times of waves propagating between pairs of locations on the solar surface are measured. Until now, the travel-time measurements have been modeled using the ray approximation, which assumes that waves travel along infinitely thin ray paths between these locations. In contrast, the scattering of the full wave field in the solar interior due to the flow is modeled in first order by the Born approximation. It is in general a more accurate model of the physics in the solar interior. In a first step, an existing model for calculating the sensitivity of travel-time measurements to solar interior flows using the Born approximation is extended from Cartesian to spherical geometry. The results are succesfully compared to the Cartesian ones and are tested for self-consistency. In a second step, the newly developed model is validated using an existing numerical simulation of linear wave propagation in the Sun. An inversion of artificial travel times for meridional flow shows excellent agreement for noiseless data and reproduces many features in the input flow profile in the case of noisy data. Finally, the new method is used to infer the deep meridional flow. I used Global Oscillation Network Group (GONG) data that were earlier analyzed using the ray approximation and I employed the same Substractive Optimized Local Averaging (SOLA) inversion technique as in the earlier study. Using an existing formula for the covariance of travel-time measurements, it is shown that the assumption of uncorrelated errors
An algebra-based method for inferring gene regulatory networks.
Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard
2014-03-26
The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the
Intracranial EEG correlates of implicit relational inference within the hippocampus.
Reber, T P; Do Lam, A T A; Axmacher, N; Elger, C E; Helmstaedter, C; Henke, K; Fell, J
2016-01-01
Drawing inferences from past experiences enables adaptive behavior in future situations. Inference has been shown to depend on hippocampal processes. Usually, inference is considered a deliberate and effortful mental act which happens during retrieval, and requires the focus of our awareness. Recent fMRI studies hint at the possibility that some forms of hippocampus-dependent inference can also occur during encoding and possibly also outside of awareness. Here, we sought to further explore the feasibility of hippocampal implicit inference, and specifically address the temporal evolution of implicit inference using intracranial EEG. Presurgical epilepsy patients with hippocampal depth electrodes viewed a sequence of word pairs, and judged the semantic fit between two words in each pair. Some of the word pairs entailed a common word (e.g., "winter-red," "red-cat") such that an indirect relation was established in following word pairs (e.g., "winter-cat"). The behavioral results suggested that drawing inference implicitly from past experience is feasible because indirect relations seemed to foster "fit" judgments while the absence of indirect relations fostered "do not fit" judgments, even though the participants were unaware of the indirect relations. A event-related potential (ERP) difference emerging 400 ms post-stimulus was evident in the hippocampus during encoding, suggesting that indirect relations were already established automatically during encoding of the overlapping word pairs. Further ERP differences emerged later post-stimulus (1,500 ms), were modulated by the participants' responses and were evident during encoding and test. Furthermore, response-locked ERP effects were evident at test. These ERP effects could hence be a correlate of the interaction of implicit memory with decision-making. Together, the data map out a time-course in which the hippocampus automatically integrates memories from discrete but related episodes to implicitly influence future
The anatomy of choice: active inference and agency
Directory of Open Access Journals (Sweden)
Karl eFriston
2013-09-01
Full Text Available This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behaviour. In particular, we consider prior beliefs that action minimises the Kullback-Leibler divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimises a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimising free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action – constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualises optimal decision theory and economic (utilitarian formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimisation, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria has a unique and Bayes-optimal solution – that minimises free energy. This sensitivity corresponds to the precision of beliefs about behaviour, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behaviour entails a representation of confidence about outcomes that are under an agent's control.
Reinforcement and inference in cross-situational word learning.
Tilles, Paulo F C; Fontanari, José F
2013-01-01
Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.
Cortical hierarchies perform Bayesian causal inference in multisensory perception.
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Tim Rohe
2015-02-01
Full Text Available To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI, and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation. At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion. Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.
Data-driven inference for the spatial scan statistic
Directory of Open Access Journals (Sweden)
Duczmal Luiz H
2011-08-01
Full Text Available Abstract Background Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. Results A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. Conclusions A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.
Reward inference by primate prefrontal and striatal neurons.
Pan, Xiaochuan; Fan, Hongwei; Sawa, Kosuke; Tsuda, Ichiro; Tsukada, Minoru; Sakagami, Masamichi
2014-01-22
The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Importantly, these LPFC neurons could predict the reward value of a stimulus using transitive inference even when the monkeys had not yet learned the stimulus-reward association directly; whereas these striatal neurons did not show such an ability. Nevertheless, because there were two set amounts of reward (large and small), the selected striatal neurons were able to exclusively infer the reward value (e.g., large) of one novel stimulus from a pair after directly experiencing the alternative stimulus with the other reward value (e.g., small). Our results suggest that although neurons that predict reward value for old stimuli in the LPFC could also do so for new stimuli via transitive inference, those in the striatum could only predict reward for new stimuli via exclusive inference. Moreover, the striatum showed more complex functions than was surmised previously for model-free learning.
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.
The anatomy of choice: active inference and agency.
Friston, Karl; Schwartenbeck, Philipp; Fitzgerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J
2013-01-01
This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.
Universal Darwinism as a process of Bayesian inference
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.
Data-driven inference for the spatial scan statistic.
Almeida, Alexandre C L; Duarte, Anderson R; Duczmal, Luiz H; Oliveira, Fernando L P; Takahashi, Ricardo H C
2011-08-02
Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.
Does bad inference drive out good?
Marozzi, Marco
2015-07-01
The (mis)use of statistics in practice is widely debated, and a field where the debate is particularly active is medicine. Many scholars emphasize that a large proportion of published medical research contains statistical errors. It has been noted that top class journals like Nature Medicine and The New England Journal of Medicine publish a considerable proportion of papers that contain statistical errors and poorly document the application of statistical methods. This paper joins the debate on the (mis)use of statistics in the medical literature. Even though the validation process of a statistical result may be quite elusive, a careful assessment of underlying assumptions is central in medicine as well as in other fields where a statistical method is applied. Unfortunately, a careful assessment of underlying assumptions is missing in many papers, including those published in top class journals. In this paper, it is shown that nonparametric methods are good alternatives to parametric methods when the assumptions for the latter ones are not satisfied. A key point to solve the problem of the misuse of statistics in the medical literature is that all journals have their own statisticians to review the statistical method/analysis section in each submitted paper. © 2015 Wiley Publishing Asia Pty Ltd.
Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro
2017-01-01
Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in…
Implementing and analyzing the multi-threaded LP-inference
Bolotova, S. Yu; Trofimenko, E. V.; Leschinskaya, M. V.
2018-03-01
The logical production equations provide new possibilities for the backward inference optimization in intelligent production-type systems. The strategy of a relevant backward inference is aimed at minimization of a number of queries to external information source (either to a database or an interactive user). The idea of the method is based on the computing of initial preimages set and searching for the true preimage. The execution of each stage can be organized independently and in parallel and the actual work at a given stage can also be distributed between parallel computers. This paper is devoted to the parallel algorithms of the relevant inference based on the advanced scheme of the parallel computations “pipeline” which allows to increase the degree of parallelism. The author also provides some details of the LP-structures implementation.
Congested Link Inference Algorithms in Dynamic Routing IP Network
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Yu Chen
2017-01-01
Full Text Available The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP and Rest Bayesian Network Model (RBNM, we proposed an Improved CLINK (ICLINK algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.
Inference of Large Phylogenies Using Neighbour-Joining
DEFF Research Database (Denmark)
Simonsen, Martin; Mailund, Thomas; Pedersen, Christian Nørgaard Storm
2011-01-01
The neighbour-joining method is a widely used method for phylogenetic reconstruction which scales to thousands of taxa. However, advances in sequencing technology have made data sets with more than 10,000 related taxa widely available. Inference of such large phylogenies takes hours or days using...... the Neighbour-Joining method on a normal desktop computer because of the O(n^3) running time. RapidNJ is a search heuristic which reduce the running time of the Neighbour-Joining method significantly but at the cost of an increased memory consumption making inference of large phylogenies infeasible. We present...... two extensions for RapidNJ which reduce the memory requirements and \\makebox{allows} phylogenies with more than 50,000 taxa to be inferred efficiently on a desktop computer. Furthermore, an improved version of the search heuristic is presented which reduces the running time of RapidNJ on many data...
Information-Theoretic Inference of Large Transcriptional Regulatory Networks
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Meyer Patrick
2007-01-01
Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.
Information-Theoretic Inference of Large Transcriptional Regulatory Networks
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Patrick E. Meyer
2007-06-01
Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.
Baselines and test data for cross-lingual inference
DEFF Research Database (Denmark)
Agic, Zeljko; Schluter, Natalie
2018-01-01
The recent years have seen a revival of interest in textual entailment, sparked by i) the emergence of powerful deep neural network learners for natural language processing and ii) the timely development of large-scale evaluation datasets such as SNLI. Recast as natural language inference......, the problem now amounts to detecting the relation between pairs of statements: they either contradict or entail one another, or they are mutually neutral. Current research in natural language inference is effectively exclusive to English. In this paper, we propose to advance the research in SNLI-style natural...... language inference toward multilingual evaluation. To that end, we provide test data for four major languages: Arabic, French, Spanish, and Russian. We experiment with a set of baselines. Our systems are based on cross-lingual word embeddings and machine translation. While our best system scores an average...
Inferring Human Mobility from Sparse Low Accuracy Mobile Sensing Data
DEFF Research Database (Denmark)
Cuttone, Andrea; Jørgensen, Sune Lehmann; Larsen, Jakob Eg
2014-01-01
Understanding both collective and personal human mobility is a central topic in Computational Social Science. Smartphone sensing data is emerging as a promising source for studying human mobility. However, most literature focuses on high-precision GPS positioning and high-frequency sampling, which...... is not always feasible in a longitudinal study or for everyday applications because location sensing has a high battery cost. In this paper we study the feasibility of inferring human mobility from sparse, low accuracy mobile sensing data. We validate our results using participants' location diaries......, and analyze the inferred geographical networks, the time spent at different places, and the number of unique places over time. Our results suggest that low resolution data allows accurate inference of human mobility patterns....
SPEEDY: An Eclipse-based IDE for invariant inference
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David R. Cok
2014-04-01
Full Text Available SPEEDY is an Eclipse-based IDE for exploring techniques that assist users in generating correct specifications, particularly including invariant inference algorithms and tools. It integrates with several back-end tools that propose invariants and will incorporate published algorithms for inferring object and loop invariants. Though the architecture is language-neutral, current SPEEDY targets C programs. Building and using SPEEDY has confirmed earlier experience demonstrating the importance of showing and editing specifications in the IDEs that developers customarily use, automating as much of the production and checking of specifications as possible, and showing counterexample information directly in the source code editing environment. As in previous work, automation of specification checking is provided by back-end SMT solvers. However, reducing the effort demanded of software developers using formal methods also requires a GUI design that guides users in writing, reviewing, and correcting specifications and automates specification inference.
Inferring time-varying network topologies from gene expression data.
Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas
2007-01-01
Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.
Paradoxical versus modulated conditional inferences: An explanation from the Stoicism
Directory of Open Access Journals (Sweden)
Miguel López-Astorga
Full Text Available Abstract According to standard propositional logic, the inferences in which the conditional introduction rule is used are absolutely correct. However, people do not always accept inferences of that kind. Orenes and Johnson-Laird carried out interesting experiments in this way and, based on the general framework of the mental models theory, explained clearly in which cases and under which circumstances such inferences are accepted and rejected. The goals of this paper are both to better understand some aspects of Stoic logic and to check whether or not that very logic can also offer an account on this issue. My conclusions are that, indeed, this later logic can do that, and that the results obtained by Orenes and Johnson-Laird can be explained based on the information that the sources provide on Stoic logic.
Inferring time derivatives including cell growth rates using Gaussian processes
Swain, Peter S.; Stevenson, Keiran; Leary, Allen; Montano-Gutierrez, Luis F.; Clark, Ivan B. N.; Vogel, Jackie; Pilizota, Teuta
2016-12-01
Often the time derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population's growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time derivatives as a function of time from time-series data. Our approach is based on Gaussian processes and applies to a wide range of data. In tests, the method is at least as accurate as others, but has several advantages: it estimates errors both in the inference and in any summary statistics, such as lag times, and allows interpolation with the corresponding error estimation. As illustrations, we infer growth rates of microbial cells, the rate of assembly of an amyloid fibril and both the speed and acceleration of two separating spindle pole bodies. Our algorithm should thus be broadly applicable.
Preschoolers can infer general rules governing fantastical events in fiction.
Van de Vondervoort, Julia W; Friedman, Ori
2014-05-01
Young children are frequently exposed to fantastic fiction. How do they make sense of the unrealistic and impossible events that occur in such fiction? Although children could view such events as isolated episodes, the present experiments suggest that children use such events to infer general fantasy rules. In 2 experiments, 2- to 4-year-olds were shown scenarios in which 2 animals behaved unrealistically (N = 78 in Experiment 1, N = 94 in Experiment 2). When asked to predict how other animals in the fiction would behave, children predicted novel behaviors consistent with the nature of the fiction. These findings suggest that preschoolers can infer the general rules that govern the events and entities in fantastic fiction and can use these rules to predict what events will happen in the fiction. The findings also provide evidence that children may infer fantasy rules at a more superordinate level than the basic level. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Inference of population history and patterns from molecular data
DEFF Research Database (Denmark)
Tataru, Paula
, the existing mathematical models and computational methods need to be reformulated. I address this from an inference perspective in two areas of bioinformatics. Population genetics studies the influence exerted by various factors on the dynamics of a population's genetic variation. These factors cover...... evolutionary forces, such as mutation and selection, but also changes in population size. The aim in population genetics is to untangle the history of a population from observed genetic variation. This subject is dominated by two dual models, the Wright-Fisher and coalescent. I first introduce a new...... approximation to the Wright-Fisher model, which I show to accurately infer split times between populations. This approximation can potentially be applied for inference of mutation rates and selection coefficients. I then illustrate how the coalescent process is the natural framework for detecting traces...
Models for probability and statistical inference theory and applications
Stapleton, James H
2007-01-01
This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readersModels for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses mo...
Hierarchical Active Inference: A Theory of Motivated Control.
Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl J
2018-04-01
Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Grouping preprocess for haplotype inference from SNP and CNV data
International Nuclear Information System (INIS)
Shindo, Hiroyuki; Chigira, Hiroshi; Nagaoka, Tomoyo; Inoue, Masato; Kamatani, Naoyuki
2009-01-01
The method of statistical haplotype inference is an indispensable technique in the field of medical science. The authors previously reported Hardy-Weinberg equilibrium-based haplotype inference that could manage single nucleotide polymorphism (SNP) data. We recently extended the method to cover copy number variation (CNV) data. Haplotype inference from mixed data is important because SNPs and CNVs are occasionally in linkage disequilibrium. The idea underlying the proposed method is simple, but the algorithm for it needs to be quite elaborate to reduce the calculation cost. Consequently, we have focused on the details on the algorithm in this study. Although the main advantage of the method is accuracy, in that it does not use any approximation, its main disadvantage is still the calculation cost, which is sometimes intractable for large data sets with missing values.
Grouping preprocess for haplotype inference from SNP and CNV data
Energy Technology Data Exchange (ETDEWEB)
Shindo, Hiroyuki; Chigira, Hiroshi; Nagaoka, Tomoyo; Inoue, Masato [Department of Electrical Engineering and Bioscience, School of Advanced Science and Engineering, Waseda University, 3-4-1, Okubo, Shinjuku-ku, Tokyo 169-8555 (Japan); Kamatani, Naoyuki, E-mail: masato.inoue@eb.waseda.ac.j [Institute of Rheumatology, Tokyo Women' s Medical University, 10-22, Kawada-cho, Shinjuku-ku, Tokyo 162-0054 (Japan)
2009-12-01
The method of statistical haplotype inference is an indispensable technique in the field of medical science. The authors previously reported Hardy-Weinberg equilibrium-based haplotype inference that could manage single nucleotide polymorphism (SNP) data. We recently extended the method to cover copy number variation (CNV) data. Haplotype inference from mixed data is important because SNPs and CNVs are occasionally in linkage disequilibrium. The idea underlying the proposed method is simple, but the algorithm for it needs to be quite elaborate to reduce the calculation cost. Consequently, we have focused on the details on the algorithm in this study. Although the main advantage of the method is accuracy, in that it does not use any approximation, its main disadvantage is still the calculation cost, which is sometimes intractable for large data sets with missing values.
Technical Note: How to use Winbugs to infer animal models
DEFF Research Database (Denmark)
Damgaard, Lars Holm
2007-01-01
This paper deals with Bayesian inferences of animal models using Gibbs sampling. First, we suggest a general and efficient method for updating additive genetic effects, in which the computational cost is independent of the pedigree depth and increases linearly only with the size of the pedigree....... Second, we show how this approach can be used to draw inferences from a wide range of animal models using the computer package Winbugs. Finally, we illustrate the approach in a simulation study, in which the data are generated and analyzed using Winbugs according to a linear model with i.i.d errors...... having Student's t distributions. In conclusion, Winbugs can be used to make inferences in small-sized, quantitative, genetic data sets applying a wide range of animal models that are not yet standard in the animal breeding literature...
Reliability of dose volume constraint inference from clinical data
Lutz, C. M.; Møller, D. S.; Hoffmann, L.; Knap, M. M.; Alber, M.
2017-04-01
Dose volume histogram points (DVHPs) frequently serve as dose constraints in radiotherapy treatment planning. An experiment was designed to investigate the reliability of DVHP inference from clinical data for multiple cohort sizes and complication incidence rates. The experimental background was radiation pneumonitis in non-small cell lung cancer and the DVHP inference method was based on logistic regression. From 102 NSCLC real-life dose distributions and a postulated DVHP model, an ‘ideal’ cohort was generated where the most predictive model was equal to the postulated model. A bootstrap and a Cohort Replication Monte Carlo (CoRepMC) approach were applied to create 1000 equally sized populations each. The cohorts were then analyzed to establish inference frequency distributions. This was applied to nine scenarios for cohort sizes of 102 (1), 500 (2) to 2000 (3) patients (by sampling with replacement) and three postulated DVHP models. The Bootstrap was repeated for a ‘non-ideal’ cohort, where the most predictive model did not coincide with the postulated model. The Bootstrap produced chaotic results for all models of cohort size 1 for both the ideal and non-ideal cohorts. For cohort size 2 and 3, the distributions for all populations were more concentrated around the postulated DVHP. For the CoRepMC, the inference frequency increased with cohort size and incidence rate. Correct inference rates >85 % were only achieved by cohorts with more than 500 patients. Both Bootstrap and CoRepMC indicate that inference of the correct or approximate DVHP for typical cohort sizes is highly uncertain. CoRepMC results were less spurious than Bootstrap results, demonstrating the large influence that randomness in dose-response has on the statistical analysis.
The Impact of Contextual Clue Selection on Inference
Directory of Open Access Journals (Sweden)
Leila Barati
2010-05-01
Full Text Available Linguistic information can be conveyed in the form of speech and written text, but it is the content of the message that is ultimately essential for higher-level processes in language comprehension, such as making inferences and associations between text information and knowledge about the world. Linguistically, inference is the shovel that allows receivers to dig meaning out from the text with selecting different embedded contextual clues. Naturally, people with different world experiences infer similar contextual situations differently. Lack of contextual knowledge of the target language can present an obstacle to comprehension (Anderson & Lynch, 2003. This paper tries to investigate how true contextual clue selection from the text can influence listener’s inference. In the present study 60 male and female teenagers (13-19 and 60 male and female young adults (20-26 were selected randomly based on Oxford Placement Test (OPT. During the study two fiction and two non-fiction passages were read to the participants in the experimental and control groups respectively and they were given scores according to Lexile’s Score (LS[1] based on their correct inference and logical thinking ability. In general the results show that participants’ clue selection based on their personal schematic references and background knowledge differ between teenagers and young adults and influence inference and listening comprehension. [1]- This is a framework for reading and listening which matches the appropriate score to each text based on degree of difficulty of text and each text was given a Lexile score from zero to four.
F-OWL: An Inference Engine for Semantic Web
Zou, Youyong; Finin, Tim; Chen, Harry
2004-01-01
Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.
Discontinuity of maximum entropy inference and quantum phase transitions
International Nuclear Information System (INIS)
Chen, Jianxin; Ji, Zhengfeng; Yu, Nengkun; Zeng, Bei; Li, Chi-Kwong; Poon, Yiu-Tung; Shen, Yi; Zhou, Duanlu
2015-01-01
In this paper, we discuss the connection between two genuinely quantum phenomena—the discontinuity of quantum maximum entropy inference and quantum phase transitions at zero temperature. It is shown that the discontinuity of the maximum entropy inference of local observable measurements signals the non-local type of transitions, where local density matrices of the ground state change smoothly at the transition point. We then propose to use the quantum conditional mutual information of the ground state as an indicator to detect the discontinuity and the non-local type of quantum phase transitions in the thermodynamic limit. (paper)
Simultaneous inference for model averaging of derived parameters
DEFF Research Database (Denmark)
Jensen, Signe Marie; Ritz, Christian
2015-01-01
Model averaging is a useful approach for capturing uncertainty due to model selection. Currently, this uncertainty is often quantified by means of approximations that do not easily extend to simultaneous inference. Moreover, in practice there is a need for both model averaging and simultaneous...... inference for derived parameters calculated in an after-fitting step. We propose a method for obtaining asymptotically correct standard errors for one or several model-averaged estimates of derived parameters and for obtaining simultaneous confidence intervals that asymptotically control the family...
Indirect Inference for Stochastic Differential Equations Based on Moment Expansions
Ballesio, Marco
2016-01-06
We provide an indirect inference method to estimate the parameters of timehomogeneous scalar diffusion and jump diffusion processes. We obtain a system of ODEs that approximate the time evolution of the first two moments of the process by the approximation of the stochastic model applying a second order Taylor expansion of the SDE s infinitesimal generator in the Dynkin s formula. This method allows a simple and efficient procedure to infer the parameters of such stochastic processes given the data by the maximization of the likelihood of an approximating Gaussian process described by the two moments equations. Finally, we perform numerical experiments for two datasets arising from organic and inorganic fouling deposition phenomena.
Reliability of dose volume constraint inference from clinical data
DEFF Research Database (Denmark)
Lutz, C M; Møller, D S; Hoffmann, L
2017-01-01
Dose volume histogram points (DVHPs) frequently serve as dose constraints in radiotherapy treatment planning. An experiment was designed to investigate the reliability of DVHP inference from clinical data for multiple cohort sizes and complication incidence rates. The experimental background...... was radiation pneumonitis in non-small cell lung cancer and the DVHP inference method was based on logistic regression. From 102 NSCLC real-life dose distributions and a postulated DVHP model, an 'ideal' cohort was generated where the most predictive model was equal to the postulated model. A bootstrap...
Design Issues and Inference in Experimental L2 Research
Hudson, Thom; Llosa, Lorena
2015-01-01
Explicit attention to research design issues is essential in experimental second language (L2) research. Too often, however, such careful attention is not paid. This article examines some of the issues surrounding experimental L2 research and its relationships to causal inferences. It discusses the place of research questions and hypotheses,…
Asymptotic inference for jump diffusions with state-dependent intensity
Becheri, Gaia; Drost, Feico; Werker, Bas
2016-01-01
We establish the local asymptotic normality property for a class of ergodic parametric jump-diffusion processes with state-dependent intensity and known volatility function sampled at high frequency. We prove that the inference problem about the drift and jump parameters is adaptive with respect to
Bayesian inference with information content model check for Langevin equations
DEFF Research Database (Denmark)
Krog, Jens F. C.; Lomholt, Michael Andersen
2017-01-01
The Bayesian data analysis framework has been proven to be a systematic and effective method of parameter inference and model selection for stochastic processes. In this work we introduce an information content model check which may serve as a goodness-of-fit, like the chi-square procedure...
The Impact of Transitive Inference Operations on Mathematics ...
African Journals Online (AJOL)
This study examined the extent to which operations of transitive inference tasks have affected the mathematics problem solving abilities of pre-primary school children. Four research hypotheses were tested at 0.05 level of significance using 400 nursery school children whose ages ranged between 4.5 and 5.5 years ...
Direct Evidence for a Dual Process Model of Deductive Inference
Markovits, Henry; Brunet, Marie-Laurence; Thompson, Valerie; Brisson, Janie
2013-01-01
In 2 experiments, we tested a strong version of a dual process theory of conditional inference (cf. Verschueren et al., 2005a, 2005b) that assumes that most reasoners have 2 strategies available, the choice of which is determined by situational variables, cognitive capacity, and metacognitive control. The statistical strategy evaluates inferences…
Inferring the conservative causal core of gene regulatory networks
Directory of Open Access Journals (Sweden)
Emmert-Streib Frank
2010-09-01
Full Text Available Abstract Background Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. Results In this paper, we introduce a novel gene regulatory network inference (GRNI algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. Conclusions For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.
Inferring the conservative causal core of gene regulatory networks.
Altay, Gökmen; Emmert-Streib, Frank
2010-09-28
Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.
Inferring Demographic History Using Two-Locus Statistics.
Ragsdale, Aaron P; Gutenkunst, Ryan N
2017-06-01
Population demographic history may be learned from contemporary genetic variation data. Methods based on aggregating the statistics of many single loci into an allele frequency spectrum (AFS) have proven powerful, but such methods ignore potentially informative patterns of linkage disequilibrium (LD) between neighboring loci. To leverage such patterns, we developed a composite-likelihood framework for inferring demographic history from aggregated statistics of pairs of loci. Using this framework, we show that two-locus statistics are more sensitive to demographic history than single-locus statistics such as the AFS. In particular, two-locus statistics escape the notorious confounding of depth and duration of a bottleneck, and they provide a means to estimate effective population size based on the recombination rather than mutation rate. We applied our approach to a Zambian population of Drosophila melanogaster Notably, using both single- and two-locus statistics, we inferred a substantially lower ancestral effective population size than previous works and did not infer a bottleneck history. Together, our results demonstrate the broad potential for two-locus statistics to enable powerful population genetic inference. Copyright © 2017 by the Genetics Society of America.
Explanation in causal inference methods for mediation and interaction
VanderWeele, Tyler
2015-01-01
A comprehensive examination of methods for mediation and interaction, VanderWeele's book is the first to approach this topic from the perspective of causal inference. Numerous software tools are provided, and the text is both accessible and easy to read, with examples drawn from diverse fields. The result is an essential reference for anyone conducting empirical research in the biomedical or social sciences.
Bayesian inference for hybrid discrete-continuous stochastic kinetic models
International Nuclear Information System (INIS)
Sherlock, Chris; Golightly, Andrew; Gillespie, Colin S
2014-01-01
We consider the problem of efficiently performing simulation and inference for stochastic kinetic models. Whilst it is possible to work directly with the resulting Markov jump process (MJP), computational cost can be prohibitive for networks of realistic size and complexity. In this paper, we consider an inference scheme based on a novel hybrid simulator that classifies reactions as either ‘fast’ or ‘slow’ with fast reactions evolving as a continuous Markov process whilst the remaining slow reaction occurrences are modelled through a MJP with time-dependent hazards. A linear noise approximation (LNA) of fast reaction dynamics is employed and slow reaction events are captured by exploiting the ability to solve the stochastic differential equation driving the LNA. This simulation procedure is used as a proposal mechanism inside a particle MCMC scheme, thus allowing Bayesian inference for the model parameters. We apply the scheme to a simple application and compare the output with an existing hybrid approach and also a scheme for performing inference for the underlying discrete stochastic model. (paper)
Inferring probabilistic stellar rotation periods using Gaussian processes
Angus, Ruth; Morton, Timothy; Aigrain, Suzanne; Foreman-Mackey, Daniel; Rajpaul, Vinesh
2018-02-01
Variability in the light curves of spotted, rotating stars is often non-sinusoidal and quasi-periodic - spots move on the stellar surface and have finite lifetimes, causing stellar flux variations to slowly shift in phase. A strictly periodic sinusoid therefore cannot accurately model a rotationally modulated stellar light curve. Physical models of stellar surfaces have many drawbacks preventing effective inference, such as highly degenerate or high-dimensional parameter spaces. In this work, we test an appropriate effective model: a Gaussian Process with a quasi-periodic covariance kernel function. This highly flexible model allows sampling of the posterior probability density function of the periodic parameter, marginalizing over the other kernel hyperparameters using a Markov Chain Monte Carlo approach. To test the effectiveness of this method, we infer rotation periods from 333 simulated stellar light curves, demonstrating that the Gaussian process method produces periods that are more accurate than both a sine-fitting periodogram and an autocorrelation function method. We also demonstrate that it works well on real data, by inferring rotation periods for 275 Kepler stars with previously measured periods. We provide a table of rotation periods for these and many more, altogether 1102 Kepler objects of interest, and their posterior probability density function samples. Because this method delivers posterior probability density functions, it will enable hierarchical studies involving stellar rotation, particularly those involving population modelling, such as inferring stellar ages, obliquities in exoplanet systems, or characterizing star-planet interactions. The code used to implement this method is available online.
Flexible Retrieval: When True Inferences Produce False Memories
Carpenter, Alexis C.; Schacter, Daniel L.
2017-01-01
Episodic memory involves flexible retrieval processes that allow us to link together distinct episodes, make novel inferences across overlapping events, and recombine elements of past experiences when imagining future events. However, the same flexible retrieval and recombination processes that underpin these adaptive functions may also leave…
Inferring Stop-Locations from WiFi
DEFF Research Database (Denmark)
Wind, David Kofoed; Sapiezynski, Piotr; Furman, Magdalena Anna
2016-01-01
methods are based exclusively on WiFi data. We study two months of WiFi data collected every two minutes by a smartphone, and infer stop-locations in the form of labelled time-intervals. For this purpose, we investigate two algorithms, both of which scale to large datasets: a greedy approach to select...
A parameter-adaptive dynamic programming approach for inferring cophylogenies
DEFF Research Database (Denmark)
Merkle, Daniel; Middendorf, Martin; Wieseke, Nicolas
2010-01-01
Background: Coevolutionary systems like hosts and their parasites are commonly used model systems for evolutionary studies. Inferring the coevolutionary history based on given phylogenies of both groups is often done by employing a set of possible types of events that happened during coevolution....
Inference of domain structure at elevated temperature in fine ...
African Journals Online (AJOL)
The thermal variation of the number of domains (nd) for Fe7S8 particles (within the size range 1-30 mm and between 20 and 300°C), has been inferred from the room temperature analytic expression between nd and particle size (L), the temperature dependences of the anisotropy energy constant (K) and the spontaneous ...
Meaningful mediation analysis : Plausible causal inference and informative communication
Pieters, Rik
2017-01-01
Statistical mediation analysis has become the technique of choice in consumer research to make causal inferences about the influence of a treatment on an outcome via one or more mediators. This tutorial aims to strengthen two weak links that impede statistical mediation analysis from reaching its
Segmentation, Inference and Classification of Partially Overlapping Nanoparticles
Chiwoo Park,; Huang, J. Z.; Ji, J. X.; Yu Ding,
2013-01-01
an agglomerate of overlapping nano-objects; infer the particle's missing contours; and ultimately, classify the particles by shape based on their complete contours. Our specific method adopts a two-stage approach: the first stage executes the task of particle
Learning What Works in Sensory Disabilities: Establishing Causal Inference
Cooney, John B.; Young, John, III; Luckner, John L.; Ferrell, Kay Alicyn
2015-01-01
This article is intended to assist teachers and researchers in designing studies that examine the efficacy of a particular intervention or strategy with students with sensory disabilities. Ten research designs that can establish causal inference (the ability to attribute any effects to the intervention) with and without randomization are discussed.
Pragmatic Inferences in Context: Learning to Interpret Contrastive Prosody
Kurumada, Chigusa; Clark, Eve V.
2017-01-01
Can preschoolers make pragmatic inferences based on the intonation of an utterance? Previous work has found that young children appear to ignore intonational meanings and come to understand contrastive intonation contours only after age six. We show that four-year-olds succeed in interpreting an English utterance, such as "It LOOKS like a…
Learning and Retention through Predictive Inference and Classification
Sakamoto, Yasuaki; Love, Bradley C.
2010-01-01
Work in category learning addresses how humans acquire knowledge and, thus, should inform classroom practices. In two experiments, we apply and evaluate intuitions garnered from laboratory-based research in category learning to learning tasks situated in an educational context. In Experiment 1, learning through predictive inference and…
Compositional rule of inference as an analogical scheme
Czech Academy of Sciences Publication Activity Database
Bouchon-Meunier, B.; Mesiar, Radko; Marsala, Ch.; Rifqi, M.
2003-01-01
Roč. 26, č. 138 (2003), s. 53-65 ISSN 0165-0114 Institutional research plan: CEZ:AV0Z1075907 Keywords : analogical scheme * compositional rule of inference * conjunctions Subject RIV: BA - General Mathematics Impact factor: 0.577, year: 2003
A linear-algebraic tool for conditional independence inference
Czech Academy of Sciences Publication Activity Database
Tanaka, K.; Studený, Milan; Takemura, A.; Sei, T.
2015-01-01
Roč. 6, č. 2 (2015), s. 150-167 ISSN 1309-3452 R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Conditional independence inference * Automated theorem proving Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2015/MTR/studeny-0450036.pdf
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...
Sensorimotor Network Crucial for Inferring Amusement from Smiles.
Paracampo, Riccardo; Tidoni, Emmanuele; Borgomaneri, Sara; di Pellegrino, Giuseppe; Avenanti, Alessio
2017-11-01
Understanding whether another's smile reflects authentic amusement is a key challenge in social life, yet, the neural bases of this ability have been largely unexplored. Here, we combined transcranial magnetic stimulation (TMS) with a novel empathic accuracy (EA) task to test whether sensorimotor and mentalizing networks are critical for understanding another's amusement. Participants were presented with dynamic displays of smiles and explicitly requested to infer whether the smiling individual was feeling authentic amusement or not. TMS over sensorimotor regions representing the face (i.e., in the inferior frontal gyrus (IFG) and ventral primary somatosensory cortex (SI)), disrupted the ability to infer amusement authenticity from observed smiles. The same stimulation did not affect performance on a nonsocial task requiring participants to track the smiling expression but not to infer amusement. Neither TMS over prefrontal and temporo-parietal areas supporting mentalizing, nor peripheral control stimulations, affected performance on either task. Thus, motor and somatosensory circuits for controlling and sensing facial movements are causally essential for inferring amusement from another's smile. These findings highlight the functional relevance of IFG and SI to amusement understanding and suggest that EA abilities may be grounded in sensorimotor networks for moving and feeling the body. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Likelihood-based inference for clustered line transect data
DEFF Research Database (Denmark)
Waagepetersen, Rasmus; Schweder, Tore
2006-01-01
The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...
Causal inference in survival analysis using pseudo-observations.
Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T
2017-07-30
Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs to address right-censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with 'once and for all' by means of so-called pseudo-observations when doing causal inference in survival analysis. The pseudo-observations can be used as a replacement of the outcomes without censoring when applying 'standard' causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non-myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3-year overall survival probability and the 3-year risk of chronic graft-versus-host disease. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Fisher information and statistical inference for phase-type distributions
DEFF Research Database (Denmark)
Bladt, Mogens; Esparza, Luz Judith R; Nielsen, Bo Friis
2011-01-01
This paper is concerned with statistical inference for both continuous and discrete phase-type distributions. We consider maximum likelihood estimation, where traditionally the expectation-maximization (EM) algorithm has been employed. Certain numerical aspects of this method are revised and we...
Behavior Intention Derivation of Android Malware Using Ontology Inference
Directory of Open Access Journals (Sweden)
Jian Jiao
2018-01-01
Full Text Available Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system.
Empirical inference festschrift in honor of Vladimir N. Vapnik
Schölkopf, Bernhard; Vovk, Vladimir
2013-01-01
This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever.
Inferences from the dark sky: Olbers' paradox revisited
International Nuclear Information System (INIS)
Arpino, Mauro; Scardigli, Fabio
2003-01-01
The classical formulation of 'Olbers' paradox' consists in looking for an explanation of the fact that the sky at night is dark. We use the experimental datum of the nocturnal darkness in order to put constraints on a Newtonian cosmological model. We infer then that the stellar system in such a model should have had an origin at a finite time in the past
Believing What You're Told: Politeness and Scalar Inferences
Directory of Open Access Journals (Sweden)
Diana Mazzarella
2018-06-01
Full Text Available The experimental pragmatics literature has extensively investigated the ways in which distinct contextual factors affect the computation of scalar inferences, whose most studied example is the one that allows “Some X-ed” to mean Not all X-ed. Recent studies from Bonnefon et al. (2009, 2011 investigate the effect of politeness on the interpretation of scalar utterances. They argue that when the scalar utterance is face-threatening (“Some people hated your speech” (i the scalar inference is less likely to be derived, and (ii the semantic interpretation of “some” (at least some is arrived at slowly and effortfully. This paper re-evaluates the role of politeness in the computation of scalar inferences by drawing on the distinction between “comprehension” and “epistemic assessment” of communicated information. In two experiments, we test the hypothesis that, in these face-threatening contexts, scalar inferences are largely derived but are less likely to be accepted as true. In line with our predictions, we find that slowdowns in the face-threatening condition are attributable to longer reaction times at the (latter epistemic assessment stage, but not at the comprehension stage.
Bayesian inference model for fatigue life of laminated composites
DEFF Research Database (Denmark)
Dimitrov, Nikolay Krasimirov; Kiureghian, Armen Der; Berggreen, Christian
2016-01-01
A probabilistic model for estimating the fatigue life of laminated composite plates is developed. The model is based on lamina-level input data, making it possible to predict fatigue properties for a wide range of laminate configurations. Model parameters are estimated by Bayesian inference. The ...
Likelihood-based inference for clustered line transect data
DEFF Research Database (Denmark)
Waagepetersen, Rasmus Plenge; Schweder, Tore
The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...
Visual Aids Improve Diagnostic Inferences and Metacognitive Judgment Calibration
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Rocio eGarcia-Retamero
2015-07-01
Full Text Available Visual aids can improve comprehension of risks associated with medical treatments, screenings, and lifestyles. Do visual aids also help decision makers accurately assess their risk comprehension? That is, do visual aids help them become well calibrated? To address these questions, we investigated the benefits of visual aids displaying numerical information and measured accuracy of self-assessment of diagnostic inferences (i.e., metacognitive judgment calibration controlling for individual differences in numeracy. Participants included 108 patients who made diagnostic inferences about three medical tests on the basis of information about the sensitivity and false-positive rate of the tests and disease prevalence. Half of the patients received the information in numbers without a visual aid, while the other half received numbers along with a grid representing the numerical information. In the numerical condition, many patients --especially those with low numeracy-- misinterpreted the predictive value of the tests and profoundly overestimated the accuracy of their inferences. Metacognitive judgment calibration mediated the relationship between numeracy and accuracy of diagnostic inferences. In contrast, in the visual aid condition, patients at all levels of numeracy showed high-levels of inferential accuracy and metacognitive judgment calibration. Results indicate that accurate metacognitive assessment may explain the beneficial effects of visual aids and numeracy --a result that accords with theory suggesting that metacognition is an essential part of risk literacy. We conclude that well-designed risk communications can inform patients about health-relevant numerical information while helping them assess the quality of their own risk comprehension.
Statistical inference for a class of multivariate negative binomial distributions
DEFF Research Database (Denmark)
Rubak, Ege Holger; Møller, Jesper; McCullagh, Peter
This paper considers statistical inference procedures for a class of models for positively correlated count variables called α-permanental random fields, and which can be viewed as a family of multivariate negative binomial distributions. Their appealing probabilistic properties have earlier been...
Active Inference, Epistemic Value, and Vicarious Trial and Error
Pezzulo, Giovanni; Cartoni, Emilio; Rigoli, Francesco; io-Lopez, Léo; Friston, Karl
2016-01-01
Balancing habitual and deliberate forms of choice entails a comparison of their respective merits--the former being faster but inflexible, and the latter slower but more versatile. Here, we show that arbitration between these two forms of control can be derived from first principles within an Active Inference scheme. We illustrate our arguments…
Bayesian inference for Markov jump processes with informative observations.
Golightly, Andrew; Wilkinson, Darren J
2015-04-01
In this paper we consider the problem of parameter inference for Markov jump process (MJP) representations of stochastic kinetic models. Since transition probabilities are intractable for most processes of interest yet forward simulation is straightforward, Bayesian inference typically proceeds through computationally intensive methods such as (particle) MCMC. Such methods ostensibly require the ability to simulate trajectories from the conditioned jump process. When observations are highly informative, use of the forward simulator is likely to be inefficient and may even preclude an exact (simulation based) analysis. We therefore propose three methods for improving the efficiency of simulating conditioned jump processes. A conditioned hazard is derived based on an approximation to the jump process, and used to generate end-point conditioned trajectories for use inside an importance sampling algorithm. We also adapt a recently proposed sequential Monte Carlo scheme to our problem. Essentially, trajectories are reweighted at a set of intermediate time points, with more weight assigned to trajectories that are consistent with the next observation. We consider two implementations of this approach, based on two continuous approximations of the MJP. We compare these constructs for a simple tractable jump process before using them to perform inference for a Lotka-Volterra system. The best performing construct is used to infer the parameters governing a simple model of motility regulation in Bacillus subtilis.
Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.
Directory of Open Access Journals (Sweden)
Heiner Stuke
2017-01-01
Full Text Available Theoretical accounts suggest that an alteration in the brain's learning mechanisms might lead to overhasty inferences, resulting in psychotic symptoms. Here, we sought to elucidate the suggested link between maladaptive learning and psychosis. Ninety-eight healthy individuals with varying degrees of delusional ideation and hallucinatory experiences performed a probabilistic reasoning task that allowed us to quantify overhasty inferences. Replicating previous results, we found a relationship between psychotic experiences and overhasty inferences during probabilistic reasoning. Computational modelling revealed that the behavioral data was best explained by a novel computational learning model that formalizes the adaptiveness of learning by a non-linear distortion of prediction error processing, where an increased non-linearity implies a growing resilience against learning from surprising and thus unreliable information (large prediction errors. Most importantly, a decreased adaptiveness of learning predicted delusional ideation and hallucinatory experiences. Our current findings provide a formal description of the computational mechanisms underlying overhasty inferences, thereby empirically substantiating theories that link psychosis to maladaptive learning.
Genealogical and evolutionary inference with the human Y chromosome.
Stumpf, M P; Goldstein, D B
2001-03-02
Population genetics has emerged as a powerful tool for unraveling human history. In addition to the study of mitochondrial and autosomal DNA, attention has recently focused on Y-chromosome variation. Ambiguities and inaccuracies in data analysis, however, pose an important obstacle to further development of the field. Here we review the methods available for genealogical inference using Y-chromosome data. Approaches can be divided into those that do and those that do not use an explicit population model in genealogical inference. We describe the strengths and weaknesses of these model-based and model-free approaches, as well as difficulties associated with the mutation process that affect both methods. In the case of genealogical inference using microsatellite loci, we use coalescent simulations to show that relatively simple generalizations of the mutation process can greatly increase the accuracy of genealogical inference. Because model-free and model-based approaches have different biases and limitations, we conclude that there is considerable benefit in the continued use of both types of approaches.
Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization
Gelman, Andrew; Lee, Daniel; Guo, Jiqiang
2015-01-01
Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…
Rational Variability in Children's Causal Inferences: The Sampling Hypothesis
Denison, Stephanie; Bonawitz, Elizabeth; Gopnik, Alison; Griffiths, Thomas L.
2013-01-01
We present a proposal--"The Sampling Hypothesis"--suggesting that the variability in young children's responses may be part of a rational strategy for inductive inference. In particular, we argue that young learners may be randomly sampling from the set of possible hypotheses that explain the observed data, producing different hypotheses with…
Bayesian Inference of High-Dimensional Dynamical Ocean Models
Lin, J.; Lermusiaux, P. F. J.; Lolla, S. V. T.; Gupta, A.; Haley, P. J., Jr.
2015-12-01
This presentation addresses a holistic set of challenges in high-dimension ocean Bayesian nonlinear estimation: i) predict the probability distribution functions (pdfs) of large nonlinear dynamical systems using stochastic partial differential equations (PDEs); ii) assimilate data using Bayes' law with these pdfs; iii) predict the future data that optimally reduce uncertainties; and (iv) rank the known and learn the new model formulations themselves. Overall, we allow the joint inference of the state, equations, geometry, boundary conditions and initial conditions of dynamical models. Examples are provided for time-dependent fluid and ocean flows, including cavity, double-gyre and Strait flows with jets and eddies. The Bayesian model inference, based on limited observations, is illustrated first by the estimation of obstacle shapes and positions in fluid flows. Next, the Bayesian inference of biogeochemical reaction equations and of their states and parameters is presented, illustrating how PDE-based machine learning can rigorously guide the selection and discovery of complex ecosystem models. Finally, the inference of multiscale bottom gravity current dynamics is illustrated, motivated in part by classic overflows and dense water formation sites and their relevance to climate monitoring and dynamics. This is joint work with our MSEAS group at MIT.
Inference of directional selection and mutation parameters assuming equilibrium.
Vogl, Claus; Bergman, Juraj
2015-12-01
In a classical study, Wright (1931) proposed a model for the evolution of a biallelic locus under the influence of mutation, directional selection and drift. He derived the equilibrium distribution of the allelic proportion conditional on the scaled mutation rate, the mutation bias and the scaled strength of directional selection. The equilibrium distribution can be used for inference of these parameters with genome-wide datasets of "site frequency spectra" (SFS). Assuming that the scaled mutation rate is low, Wright's model can be approximated by a boundary-mutation model, where mutations are introduced into the population exclusively from sites fixed for the preferred or unpreferred allelic states. With the boundary-mutation model, inference can be partitioned: (i) the shape of the SFS distribution within the polymorphic region is determined by random drift and directional selection, but not by the mutation parameters, such that inference of the selection parameter relies exclusively on the polymorphic sites in the SFS; (ii) the mutation parameters can be inferred from the amount of polymorphic and monomorphic preferred and unpreferred alleles, conditional on the selection parameter. Herein, we derive maximum likelihood estimators for the mutation and selection parameters in equilibrium and apply the method to simulated SFS data as well as empirical data from a Madagascar population of Drosophila simulans. Copyright © 2015 Elsevier Inc. All rights reserved.
Likelihood based inference for partially observed renewal processes
van Lieshout, Maria Nicolette Margaretha
2016-01-01
This paper is concerned with inference for renewal processes on the real line that are observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point process theory to propose a Monte Carlo maximum
Can facial uniqueness be inferred from impostor scores?
Dutta, A.; Veldhuis, Raymond N.J.; Spreeuwers, Lieuwe Jan
2013-01-01
In Biometrics, facial uniqueness is commonly inferred from impostor similarity scores. In this paper, we show that such uniqueness measures are highly unstable in the presence of image quality variations like pose, noise and blur. We also experimentally demonstrate the instability of a recently
Gravity inferred subsurface structure of Gadwal schist belt, Andhra ...
Indian Academy of Sciences (India)
residual gravity profile data were interpreted using 2-D prism models. The results ... Geological and geophysical layout map of the Gadwal schist belt area, Andhra Pradesh (after Ananda Murty and ... Observed gravity (Bouguer) values, regional, residual and inferred gravity models along traverse I of the Gadwal schist.
Bayesian Inference for Linear Parabolic PDEs with Noisy Boundary Conditions
Ruggeri, Fabrizio; Sawlan, Zaid A; Scavino, Marco; Tempone, Raul
2015-01-01
have been assumed for the time-dependent Dirichlet boundary values. Our approach is applied to synthetic data for the one-dimensional heat equation model, where the thermal diffusivity is the unknown parameter. We show how to infer the thermal
Do Preschool Teachers Consider Inferences for Book Discussions?
Scheiner, Esther Y.; Gorsetman, Chaya
2009-01-01
This study examined whether or not preschool teachers attended to instances requiring inference-making in story books. Thirty-one preschool teachers were asked to read three fictional story books and to identify parts of the story that would be difficult for young children to interpret. Additionally, they were asked to construct questions that…
Speaker Reliability Guides Children's Inductive Inferences about Novel Properties
Kim, Sunae; Kalish, Charles W.; Harris, Paul L.
2012-01-01
Prior work shows that children can make inductive inferences about objects based on their labels rather than their appearance (Gelman, 2003). A separate line of research shows that children's trust in a speaker's label is selective. Children accept labels from a reliable speaker over an unreliable speaker (e.g., Koenig & Harris, 2005). In the…
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
Functional networks inference from rule-based machine learning models.
Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume
2016-01-01
Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The
Essays on inference in economics, competition, and the rate of profit
Scharfenaker, Ellis S.
This dissertation is comprised of three papers that demonstrate the role of Bayesian methods of inference and Shannon's information theory in classical political economy. The first chapter explores the empirical distribution of profit rate data from North American firms from 1962-2012. This chapter address the fact that existing methods for sample selection from noisy profit rate data in the industrial organization field of economics tends to be conditional on a covariate's value that risks discarding information. Conditioning sample selection instead on the profit rate data's structure by means of a two component (signal and noise) Bayesian mixture model we find the the profit rate sample to be time stationary Laplace distributed, corroborating earlier estimates of cross section distributions. The second chapter compares alternative probabilistic approaches to discrete (quantal) choice analysis and examines the various ways in which they overlap. In particular, the work on individual choice behavior by Duncan Luce and the extension of this work to quantal response problems by game theoreticians is shown to be related both to the rational inattention work of Christopher Sims through Shannon's information theory as well as to the maximum entropy principle of inference proposed physicist Edwin T. Jaynes. In the third chapter I propose a model of ``classically" competitive firms facing informational entropy constraints in their decisions to potentially enter or exit markets based on profit rate differentials. The result is a three parameter logit quantal response distribution for firm entry and exit decisions. Bayesian methods are used for inference into the the distribution of entry and exit decisions conditional on profit rate deviations and firm level data from Compustat is used to test these predictions.
A grammar inference approach for predicting kinase specific phosphorylation sites.
Datta, Sutapa; Mukhopadhyay, Subhasis
2015-01-01
Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner.
A Grammar Inference Approach for Predicting Kinase Specific Phosphorylation Sites
Datta, Sutapa; Mukhopadhyay, Subhasis
2015-01-01
Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner. PMID:25886273
Robust Demographic Inference from Genomic and SNP Data
Excoffier, Laurent; Dupanloup, Isabelle; Huerta-Sánchez, Emilia; Sousa, Vitor C.; Foll, Matthieu
2013-01-01
We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with , the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets. PMID:24204310
Minnesota Department of Natural Resources — FEMA Cross Sections are required for any Digital Flood Insurance Rate Map database where cross sections are shown on the Flood Insurance Rate Map (FIRM). Normally...
Causal inference and longitudinal data: a case study of religion and mental health.
VanderWeele, Tyler J; Jackson, John W; Li, Shanshan
2016-11-01
We provide an introduction to causal inference with longitudinal data and discuss the complexities of analysis and interpretation when exposures can vary over time. We consider what types of causal questions can be addressed with the standard regression-based analyses and what types of covariate control and control for the prior values of outcome and exposure must be made to reason about causal effects. We also consider newer classes of causal models, including marginal structural models, that can assess questions of the joint effects of time-varying exposures and can take into account feedback between the exposure and outcome over time. Such feedback renders cross-sectional data ineffective for drawing inferences about causation. The challenges are illustrated by analyses concerning potential effects of religious service attendance on depression, in which there may in fact be effects in both directions with service attendance preventing the subsequent depression, but depression itself leading to lower levels of the subsequent religious service attendance. Longitudinal designs, with careful control for prior exposures, outcomes, and confounders, and suitable methodology, will strengthen research on mental health, religion and health, and in the biomedical and social sciences generally.
International Nuclear Information System (INIS)
Ali, S A; Kim, D-H; Cafaro, C; Giffin, A
2012-01-01
Information geometric techniques and inductive inference methods hold great promise for solving computational problems of interest in classical and quantum physics, especially with regard to complexity characterization of dynamical systems in terms of their probabilistic description on curved statistical manifolds. In this paper, we investigate the possibility of describing the macroscopic behavior of complex systems in terms of the underlying statistical structure of their microscopic degrees of freedom by the use of statistical inductive inference and information geometry. We review the maximum relative entropy formalism and the theoretical structure of the information geometrodynamical approach to chaos on statistical manifolds M S . Special focus is devoted to a description of the roles played by the sectional curvature K M S , the Jacobi field intensity J M S and the information geometrodynamical entropy S M S . These quantities serve as powerful information-geometric complexity measures of information-constrained dynamics associated with arbitrary chaotic and regular systems defined on M S . Finally, the application of such information-geometric techniques to several theoretical models is presented.
Cognitive Inference Device for Activity Supervision in the Elderly
Directory of Open Access Journals (Sweden)
Nilamadhab Mishra
2014-01-01
Full Text Available Human activity, life span, and quality of life are enhanced by innovations in science and technology. Aging individual needs to take advantage of these developments to lead a self-regulated life. However, maintaining a self-regulated life at old age involves a high degree of risk, and the elderly often fail at this goal. Thus, the objective of our study is to investigate the feasibility of implementing a cognitive inference device (CI-device for effective activity supervision in the elderly. To frame the CI-device, we propose a device design framework along with an inference algorithm and implement the designs through an artificial neural model with different configurations, mapping the CI-device’s functions to minimise the device’s prediction error. An analysis and discussion are then provided to validate the feasibility of CI-device implementation for activity supervision in the elderly.
Algorithms for MDC-Based Multi-locus Phylogeny Inference
Yu, Yun; Warnow, Tandy; Nakhleh, Luay
One of the criteria for inferring a species tree from a collection of gene trees, when gene tree incongruence is assumed to be due to incomplete lineage sorting (ILS), is minimize deep coalescence, or MDC. Exact algorithms for inferring the species tree from rooted, binary trees under MDC were recently introduced. Nevertheless, in phylogenetic analyses of biological data sets, estimated gene trees may differ from true gene trees, be incompletely resolved, and not necessarily rooted. In this paper, we propose new MDC formulations for the cases where the gene trees are unrooted/binary, rooted/non-binary, and unrooted/non-binary. Further, we prove structural theorems that allow us to extend the algorithms for the rooted/binary gene tree case to these cases in a straightforward manner. Finally, we study the performance of these methods in coalescent-based computer simulations.
Perceptual learning as improved probabilistic inference in early sensory areas.
Bejjanki, Vikranth R; Beck, Jeffrey M; Lu, Zhong-Lin; Pouget, Alexandre
2011-05-01
Extensive training on simple tasks such as fine orientation discrimination results in large improvements in performance, a form of learning known as perceptual learning. Previous models have argued that perceptual learning is due to either sharpening and amplification of tuning curves in early visual areas or to improved probabilistic inference in later visual areas (at the decision stage). However, early theories are inconsistent with the conclusions of psychophysical experiments manipulating external noise, whereas late theories cannot explain the changes in neural responses that have been reported in cortical areas V1 and V4. Here we show that we can capture both the neurophysiological and behavioral aspects of perceptual learning by altering only the feedforward connectivity in a recurrent network of spiking neurons so as to improve probabilistic inference in early visual areas. The resulting network shows modest changes in tuning curves, in line with neurophysiological reports, along with a marked reduction in the amplitude of pairwise noise correlations.
Inference and learning in sparse systems with multiple states
International Nuclear Information System (INIS)
Braunstein, A.; Ramezanpour, A.; Zhang, P.; Zecchina, R.
2011-01-01
We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost.
Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference.
Suchow, Jordan W; Bourgin, David D; Griffiths, Thomas L
2017-07-01
Evolutionary theory describes the dynamics of population change in settings affected by reproduction, selection, mutation, and drift. In the context of human cognition, evolutionary theory is most often invoked to explain the origins of capacities such as language, metacognition, and spatial reasoning, framing them as functional adaptations to an ancestral environment. However, evolutionary theory is useful for understanding the mind in a second way: as a mathematical framework for describing evolving populations of thoughts, ideas, and memories within a single mind. In fact, deep correspondences exist between the mathematics of evolution and of learning, with perhaps the deepest being an equivalence between certain evolutionary dynamics and Bayesian inference. This equivalence permits reinterpretation of evolutionary processes as algorithms for Bayesian inference and has relevance for understanding diverse cognitive capacities, including memory and creativity. Copyright © 2017 Elsevier Ltd. All rights reserved.
Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference.
Siegelmann, Hava T; Holzman, Lars E
2010-09-01
One of the brain's most basic functions is integrating sensory data from diverse sources. This ability causes us to question whether the neural system is computationally capable of intelligently integrating data, not only when sources have known, fixed relative dependencies but also when it must determine such relative weightings based on dynamic conditions, and then use these learned weightings to accurately infer information about the world. We suggest that the brain is, in fact, fully capable of computing this parallel task in a single network and describe a neural inspired circuit with this property. Our implementation suggests the possibility that evidence learning requires a more complex organization of the network than was previously assumed, where neurons have different specialties, whose emergence brings the desired adaptivity seen in human online inference.
EEG Based Inference of Spatio-Temporal Brain Dynamics
DEFF Research Database (Denmark)
Hansen, Sofie Therese
Electroencephalography (EEG) provides a measure of brain activity and has improved our understanding of the brain immensely. However, there is still much to be learned and the full potential of EEG is yet to be realized. In this thesis we suggest to improve the information gain of EEG using three...... different approaches; 1) by recovery of the EEG sources, 2) by representing and inferring the propagation path of EEG sources, and 3) by combining EEG with functional magnetic resonance imaging (fMRI). The common goal of the methods, and thus of this thesis, is to improve the spatial dimension of EEG...... recovery ability. The forward problem describes the propagation of neuronal activity in the brain to the EEG electrodes on the scalp. The geometry and conductivity of the head layers are normally required to model this path. We propose a framework for inferring forward models which is based on the EEG...
Indirect inference with time series observed with error
DEFF Research Database (Denmark)
Rossi, Eduardo; Santucci de Magistris, Paolo
estimation. We propose to solve this inconsistency by jointly estimating the nuisance and the structural parameters. Under standard assumptions, this estimator is consistent and asymptotically normal. A condition for the identification of ARMA plus noise is obtained. The proposed methodology is used......We analyze the properties of the indirect inference estimator when the observed series are contaminated by measurement error. We show that the indirect inference estimates are asymptotically biased when the nuisance parameters of the measurement error distribution are neglected in the indirect...... to estimate the parameters of continuous-time stochastic volatility models with auxiliary specifications based on realized volatility measures. Monte Carlo simulations shows the bias reduction of the indirect estimates obtained when the microstructure noise is explicitly modeled. Finally, an empirical...
Classical methods for interpreting objective function minimization as intelligent inference
Energy Technology Data Exchange (ETDEWEB)
Golden, R.M. [Univ. of Texas, Dallas, TX (United States)
1996-12-31
Most recognition algorithms and neural networks can be formally viewed as seeking a minimum value of an appropriate objective function during either classification or learning phases. The goal of this paper is to argue that in order to show a recognition algorithm is making intelligent inferences, it is not sufficient to show that the recognition algorithm is computing (or trying to compute) the global minimum of some objective function. One must explicitly define a {open_quotes}relational system{close_quotes} for the recognition algorithm or neural network which identifies the: (i) sample space, (ii) the relevant sigmafield of events generated by the sample space, and (iii) the {open_quotes}relation{close_quotes} for that relational system. Only when such a {open_quotes}relational system{close_quotes} is properly defined, is it possible to formally establish the sense in which computing the global minimum of an objective function is an intelligent, inference.
TRIP: An interactive retrieving-inferring data imputation approach
Li, Zhixu
2016-06-25
Data imputation aims at filling in missing attribute values in databases. Existing imputation approaches to nonquantitive string data can be roughly put into two categories: (1) inferring-based approaches [2], and (2) retrieving-based approaches [1]. Specifically, the inferring-based approaches find substitutes or estimations for the missing ones from the complete part of the data set. However, they typically fall short in filling in unique missing attribute values which do not exist in the complete part of the data set [1]. The retrieving-based approaches resort to external resources for help by formulating proper web search queries to retrieve web pages containing the missing values from the Web, and then extracting the missing values from the retrieved web pages [1]. This webbased retrieving approach reaches a high imputation precision and recall, but on the other hand, issues a large number of web search queries, which brings a large overhead [1]. © 2016 IEEE.
TRIP: An interactive retrieving-inferring data imputation approach
Li, Zhixu; Qin, Lu; Cheng, Hong; Zhang, Xiangliang; Zhou, Xiaofang
2016-01-01
Data imputation aims at filling in missing attribute values in databases. Existing imputation approaches to nonquantitive string data can be roughly put into two categories: (1) inferring-based approaches [2], and (2) retrieving-based approaches [1]. Specifically, the inferring-based approaches find substitutes or estimations for the missing ones from the complete part of the data set. However, they typically fall short in filling in unique missing attribute values which do not exist in the complete part of the data set [1]. The retrieving-based approaches resort to external resources for help by formulating proper web search queries to retrieve web pages containing the missing values from the Web, and then extracting the missing values from the retrieved web pages [1]. This webbased retrieving approach reaches a high imputation precision and recall, but on the other hand, issues a large number of web search queries, which brings a large overhead [1]. © 2016 IEEE.
Sign Inference for Dynamic Signed Networks via Dictionary Learning
Directory of Open Access Journals (Sweden)
Yi Cen
2013-01-01
Full Text Available Mobile online social network (mOSN is a burgeoning research area. However, most existing works referring to mOSNs deal with static network structures and simply encode whether relationships among entities exist or not. In contrast, relationships in signed mOSNs can be positive or negative and may be changed with time and locations. Applying certain global characteristics of social balance, in this paper, we aim to infer the unknown relationships in dynamic signed mOSNs and formulate this sign inference problem as a low-rank matrix estimation problem. Specifically, motivated by the Singular Value Thresholding (SVT algorithm, a compact dictionary is selected from the observed dataset. Based on this compact dictionary, the relationships in the dynamic signed mOSNs are estimated via solving the formulated problem. Furthermore, the estimation accuracy is improved by employing a dictionary self-updating mechanism.
Research designs and making causal inferences from health care studies.
Flannelly, Kevin J; Jankowski, Katherine R B
2014-01-01
This article summarizes the major types of research designs used in healthcare research, including experimental, quasi-experimental, and observational studies. Observational studies are divided into survey studies (descriptive and correlational studies), case-studies and analytic studies, the last of which are commonly used in epidemiology: case-control, retrospective cohort, and prospective cohort studies. Similarities and differences among the research designs are described and the relative strength of evidence they provide is discussed. Emphasis is placed on five criteria for drawing causal inferences that are derived from the writings of the philosopher John Stuart Mill, especially his methods or canons. The application of the criteria to experimentation is explained. Particular attention is given to the degree to which different designs meet the five criteria for making causal inferences. Examples of specific studies that have used various designs in chaplaincy research are provided.
Dopamine reward prediction errors reflect hidden state inference across time
Starkweather, Clara Kwon; Babayan, Benedicte M.; Uchida, Naoshige; Gershman, Samuel J.
2017-01-01
Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a ‘belief state’). In this work, we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling exhibited a striking difference between two tasks that differed only with respect to whether reward was delivered deterministically. Our results favor an associative learning rule that combines cached values with hidden state inference. PMID:28263301
Inference with constrained hidden Markov models in PRISM
DEFF Research Database (Denmark)
Christiansen, Henning; Have, Christian Theil; Lassen, Ole Torp
2010-01-01
A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. De......_different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment.......A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference...
Bootstrap-Based Inference for Cube Root Consistent Estimators
DEFF Research Database (Denmark)
Cattaneo, Matias D.; Jansson, Michael; Nagasawa, Kenichi
This note proposes a consistent bootstrap-based distributional approximation for cube root consistent estimators such as the maximum score estimator of Manski (1975) and the isotonic density estimator of Grenander (1956). In both cases, the standard nonparametric bootstrap is known...... to be inconsistent. Our method restores consistency of the nonparametric bootstrap by altering the shape of the criterion function defining the estimator whose distribution we seek to approximate. This modification leads to a generic and easy-to-implement resampling method for inference that is conceptually distinct...... from other available distributional approximations based on some form of modified bootstrap. We offer simulation evidence showcasing the performance of our inference method in finite samples. An extension of our methodology to general M-estimation problems is also discussed....
Mistaking geography for biology: inferring processes from species distributions.
Warren, Dan L; Cardillo, Marcel; Rosauer, Dan F; Bolnick, Daniel I
2014-10-01
Over the past few decades, there has been a rapid proliferation of statistical methods that infer evolutionary and ecological processes from data on species distributions. These methods have led to considerable new insights, but they often fail to account for the effects of historical biogeography on present-day species distributions. Because the geography of speciation can lead to patterns of spatial and temporal autocorrelation in the distributions of species within a clade, this can result in misleading inferences about the importance of deterministic processes in generating spatial patterns of biodiversity. In this opinion article, we discuss ways in which patterns of species distributions driven by historical biogeography are often interpreted as evidence of particular evolutionary or ecological processes. We focus on three areas that are especially prone to such misinterpretations: community phylogenetics, environmental niche modelling, and analyses of beta diversity (compositional turnover of biodiversity). Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.
STUDENT PREDICTION SYSTEM FOR PLACEMENT TRAINING USING FUZZY INFERENCE SYSTEM
Directory of Open Access Journals (Sweden)
Ravi Kumar Rathore
2017-04-01
Full Text Available Proposed student prediction system is most vital approach which may be used to differentiate the student data/information on the basis of the student performance. Managing placement and training records in any larger organization is quite difficult as the student number are high; in such condition differentiation and classification on different categories becomes tedious. Proposed fuzzy inference system will classify the student data with ease and will be helpful to many educational organizations. There are lots of classification algorithms and statistical base technique which may be taken as good assets for classify the student data set in the education field. In this paper, Fuzzy Inference system has been applied to predict student performance which will help to identify performance of the students and also provides an opportunity to improve to performance. For instance, here we will classify the student’s data set for placement and non-placement classes.
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
Qualitative reasoning for biological network inference from systematic perturbation experiments.
Badaloni, Silvana; Di Camillo, Barbara; Sambo, Francesco
2012-01-01
The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.
Rational Inference of Beliefs and Desires From Emotional Expressions.
Wu, Yang; Baker, Chris L; Tenenbaum, Joshua B; Schulz, Laura E
2018-04-01
We investigated people's ability to infer others' mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents' desires throughout. When the agent observed, but did not cause the outcome, participants' ability to recover the agent's beliefs depended on the evidence they got (i.e., her reaction only to the actual outcome or to both the expected and actual outcomes; Experiments 1 and 2). When the agent caused the event, participants' judgments also depended on the probability of the action (Experiments 3 and 4); when actions were improbable given the mental states, people failed to recover the agent's beliefs even when they saw her react to both the anticipated and actual outcomes. A Bayesian model captured human performance throughout (rs ≥ .95), consistent with the proposal that people rationally integrate information about others' actions and emotional reactions to infer their unobservable mental states. Copyright © 2017 Cognitive Science Society, Inc.
Application of Bayesian inference to stochastic analytic continuation
International Nuclear Information System (INIS)
Fuchs, S; Pruschke, T; Jarrell, M
2010-01-01
We present an algorithm for the analytic continuation of imaginary-time quantum Monte Carlo data. The algorithm is strictly based on principles of Bayesian statistical inference. It utilizes Monte Carlo simulations to calculate a weighted average of possible energy spectra. We apply the algorithm to imaginary-time quantum Monte Carlo data and compare the resulting energy spectra with those from a standard maximum entropy calculation.
Inferring on the Intentions of Others by Hierarchical Bayesian Learning
Diaconescu, Andreea O.; Mathys, Christoph; Weber, Lilian A. E.; Daunizeau, Jean; Kasper, Lars; Lomakina, Ekaterina I.; Fehr, Ernst; Stephan, Klaas E.
2014-01-01
Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition. PMID:25187943
An Intelligent Inference System for Robot Hand Optimal Grasp Preshaping
Directory of Open Access Journals (Sweden)
Cabbar Veysel Baysal
2010-11-01
Full Text Available This paper presents a novel Intelligent Inference System (IIS for the determination of an optimum preshape for multifingered robot hand grasping, given object under a manipulation task. The IIS is formed as hybrid agent architecture, by the synthesis of object properties, manipulation task characteristics, grasp space partitioning, lowlevel kinematical analysis, evaluation of contact wrench patterns via fuzzy approximate reasoning and ANN structure for incremental learning. The IIS is implemented in software with a robot hand simulation.
The effect of nonstationarity on models inferred from neural data
International Nuclear Information System (INIS)
Tyrcha, Joanna; Roudi, Yasser; Marsili, Matteo; Hertz, John
2013-01-01
Neurons subject to a common nonstationary input may exhibit a correlated firing behavior. Correlations in the statistics of neural spike trains also arise as the effect of interaction between neurons. Here we show that these two situations can be distinguished with machine learning techniques, provided that the data are rich enough. In order to do this, we study the problem of inferring a kinetic Ising model, stationary or nonstationary, from the available data. We apply the inference procedure to two data sets: one from salamander retinal ganglion cells and the other from a realistic computational cortical network model. We show that many aspects of the concerted activity of the salamander retinal neurons can be traced simply to the external input. A model of non-interacting neurons subject to a nonstationary external field outperforms a model with stationary input with couplings between neurons, even accounting for the differences in the number of model parameters. When couplings are added to the nonstationary model, for the retinal data, little is gained: the inferred couplings are generally not significant. Likewise, the distribution of the sizes of sets of neurons that spike simultaneously and the frequency of spike patterns as a function of their rank (Zipf plots) are well explained by an independent-neuron model with time-dependent external input, and adding connections to such a model does not offer significant improvement. For the cortical model data, robust couplings, well correlated with the real connections, can be inferred using the nonstationary model. Adding connections to this model slightly improves the agreement with the data for the probability of synchronous spikes but hardly affects the Zipf plot. (paper)
Kernel learning at the first level of inference.
Cawley, Gavin C; Talbot, Nicola L C
2014-05-01
Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.
The effect of nonstationarity on models inferred from neural data
Energy Technology Data Exchange (ETDEWEB)
Tyrcha, Joanna [Department of Mathematical Statistics, Stockholm University, SE-10691 Stockholm (Sweden); Roudi, Yasser [Kavli Institute for Systems Neuroscience, NTNU, NO-7010 Trondheim (Norway); Marsili, Matteo [The Abdus Salam ICTP, Strada Costiera 11, I-34151, Trieste (Italy); Hertz, John [Nordita, Royal Institute of Technology and Stockholm University, SE-106 91 Stockholm (Sweden)
2013-03-01
Neurons subject to a common nonstationary input may exhibit a correlated firing behavior. Correlations in the statistics of neural spike trains also arise as the effect of interaction between neurons. Here we show that these two situations can be distinguished with machine learning techniques, provided that the data are rich enough. In order to do this, we study the problem of inferring a kinetic Ising model, stationary or nonstationary, from the available data. We apply the inference procedure to two data sets: one from salamander retinal ganglion cells and the other from a realistic computational cortical network model. We show that many aspects of the concerted activity of the salamander retinal neurons can be traced simply to the external input. A model of non-interacting neurons subject to a nonstationary external field outperforms a model with stationary input with couplings between neurons, even accounting for the differences in the number of model parameters. When couplings are added to the nonstationary model, for the retinal data, little is gained: the inferred couplings are generally not significant. Likewise, the distribution of the sizes of sets of neurons that spike simultaneously and the frequency of spike patterns as a function of their rank (Zipf plots) are well explained by an independent-neuron model with time-dependent external input, and adding connections to such a model does not offer significant improvement. For the cortical model data, robust couplings, well correlated with the real connections, can be inferred using the nonstationary model. Adding connections to this model slightly improves the agreement with the data for the probability of synchronous spikes but hardly affects the Zipf plot. (paper)
Progress on Bayesian Inference of the Fast Ion Distribution Function
DEFF Research Database (Denmark)
Stagner, L.; Heidbrink, W.W,; Chen, X.
2013-01-01
. 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...... sensitivity of the measurements are incorporated into Bayesian likelihood probabilities. Prior probabilities describe physical constraints. This poster will show reconstructions of classically described, low-power, MHD-quiescent distribution functions from actual FIDA measurements. A description of the full...
Reward Inference by Primate Prefrontal and Striatal Neurons
Pan, Xiaochuan; Fan, Hongwei; Sawa, Kosuke; Tsuda, Ichiro; Tsukada, Minoru; Sakagami, Masamichi
2014-01-01
The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Im...
Quantitative DMS mapping for automated RNA secondary structure inference
Cordero, Pablo; Kladwang, Wipapat; VanLang, Christopher C.; Das, Rhiju
2012-01-01
For decades, dimethyl sulfate (DMS) mapping has informed manual modeling of RNA structure in vitro and in vivo. Here, we incorporate DMS data into automated secondary structure inference using a pseudo-energy framework developed for 2'-OH acylation (SHAPE) mapping. On six non-coding RNAs with crystallographic models, DMS- guided modeling achieves overall false negative and false discovery rates of 9.5% and 11.6%, comparable or better than SHAPE-guided modeling; and non-parametric bootstrappin...
Exact nonparametric inference for detection of nonlinear determinism
Luo, Xiaodong; Zhang, Jie; Small, Michael; Moroz, Irene
2005-01-01
We propose an exact nonparametric inference scheme for the detection of nonlinear determinism. The essential fact utilized in our scheme is that, for a linear stochastic process with jointly symmetric innovations, its ordinary least square (OLS) linear prediction error is symmetric about zero. Based on this viewpoint, a class of linear signed rank statistics, e.g. the Wilcoxon signed rank statistic, can be derived with the known null distributions from the prediction error. Thus one of the ad...
Cognitive Inference Device for Activity Supervision in the Elderly
Mishra, Nilamadhab; Lin, Chung-Chih; Chang, Hsien-Tsung
2014-01-01
Human activity, life span, and quality of life are enhanced by innovations in science and technology. Aging individual needs to take advantage of these developments to lead a self-regulated life. However, maintaining a self-regulated life at old age involves a high degree of risk, and the elderly often fail at this goal. Thus, the objective of our study is to investigate the feasibility of implementing a cognitive inference device (CI-device) for effective activity supervision in the elderly....
Word Similarity from Dictionaries: Inferring Fuzzy Measures from Fuzzy Graphs
Directory of Open Access Journals (Sweden)
Vicenc Torra
2008-01-01
Full Text Available WORD SIMILARITY FROM DICTIONARIES: INFERRING FUZZY MEASURES FROM FUZZY GRAPHS The computation of similarities between words is a basic element of information retrieval systems, when retrieval is not solely based on word matching. In this work we consider a measure between words based on dictionaries. This is achieved assuming that a dictionary is formalized as a fuzzy graph. We show that the approach permits to compute measures not only for pairs of words but for sets of them.
Practical Statistics for LHC Physicists: Bayesian Inference (3/3)
CERN. Geneva
2015-01-01
These lectures cover those principles and practices of statistics that are most relevant for work at the LHC. The first lecture discusses the basic ideas of descriptive statistics, probability and likelihood. The second lecture covers the key ideas in the frequentist approach, including confidence limits, profile likelihoods, p-values, and hypothesis testing. The third lecture covers inference in the Bayesian approach. Throughout, real-world examples will be used to illustrate the practical application of the ideas. No previous knowledge is assumed.
Practical Statistics for LHC Physicists: Frequentist Inference (2/3)
CERN. Geneva
2015-01-01
These lectures cover those principles and practices of statistics that are most relevant for work at the LHC. The first lecture discusses the basic ideas of descriptive statistics, probability and likelihood. The second lecture covers the key ideas in the frequentist approach, including confidence limits, profile likelihoods, p-values, and hypothesis testing. The third lecture covers inference in the Bayesian approach. Throughout, real-world examples will be used to illustrate the practical application of the ideas. No previous knowledge is assumed.
An Algebraic Approach to Inference in Complex Networked Structures
2015-07-09
44], [45],[46] where the shift is the elementary non-trivial filter that generates, under an appropriate notion of shift invariance, all linear ... elementary filter, and its output is a graph signal with the value at vertex n of the graph given approximately by a weighted linear combination of...AFRL-AFOSR-VA-TR-2015-0265 An Algebraic Approach to Inference in Complex Networked Structures Jose Moura CARNEGIE MELLON UNIVERSITY Final Report 07
Statistical Inference for a Class of Multivariate Negative Binomial Distributions
DEFF Research Database (Denmark)
Rubak, Ege H.; Møller, Jesper; McCullagh, Peter
This paper considers statistical inference procedures for a class of models for positively correlated count variables called -permanental random fields, and which can be viewed as a family of multivariate negative binomial distributions. Their appealing probabilistic properties have earlier been...... studied in the literature, while this is the first statistical paper on -permanental random fields. The focus is on maximum likelihood estimation, maximum quasi-likelihood estimation and on maximum composite likelihood estimation based on uni- and bivariate distributions. Furthermore, new results...
Methodology for the inference of gene function from phenotype data.
Ascensao, Joao A; Dolan, Mary E; Hill, David P; Blake, Judith A
2014-12-12
Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures. We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function. We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes. We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and
Precursors to language: Social cognition and pragmatic inference in primates.
Seyfarth, Robert M; Cheney, Dorothy L
2017-02-01
Despite their differences, human language and the vocal communication of nonhuman primates share many features. Both constitute forms of coordinated activity, rely on many shared neural mechanisms, and involve discrete, combinatorial cognition that includes rich pragmatic inference. These common features suggest that during evolution the ancestors of all modern primates faced similar social problems and responded with similar systems of communication and cognition. When language later evolved from this common foundation, many of its distinctive features were already present.
STATISTICAL RELATIONAL LEARNING AND SCRIPT INDUCTION FOR TEXTUAL INFERENCE
2017-12-01
compensate for parser errors. We replace deterministic conjunction by an average combiner, which encodes causal independence. Our framework was the...sentence similarity (STS) and sentence paraphrasing, but not Textual Entailment, where deeper inferences are required. As the formula for conjunction ...When combined, our algorithm learns to rely on systems that not just agree on an output but also the provenance of this output in conjunction with the
Analogy in causal inference: rethinking Austin Bradford Hill's neglected consideration.
Weed, Douglas L
2018-05-01
The purpose of this article was to rethink and resurrect Austin Bradford Hill's "criterion" of analogy as an important consideration in causal inference. In epidemiology today, analogy is either completely ignored (e.g., in many textbooks), or equated with biologic plausibility or coherence, or aligned with the scientist's imagination. None of these examples, however, captures Hill's description of analogy. His words suggest that there may be something gained by contrasting two bodies of evidence, one from an established causal relationship, the other not. Coupled with developments in the methods of systematic assessments of evidence-including but not limited to meta-analysis-analogy can be restructured as a key component in causal inference. This new approach will require that a collection-a library-of known cases of causal inference (i.e., bodies of evidence involving established causal relationships) be developed. This library would likely include causal assessments by organizations such as the International Agency for Research on Cancer, the National Toxicology Program, and the United States Environmental Protection Agency. In addition, a process for describing key features of a causal relationship would need to be developed along with what will be considered paradigm cases of causation. Finally, it will be important to develop ways to objectively compare a "new" body of evidence with the relevant paradigm case of causation. Analogy, along with all other existing methods and causal considerations, may improve our ability to identify causal relationships. Copyright © 2018 Elsevier Inc. All rights reserved.
A neuro-fuzzy inference system for sensor monitoring
International Nuclear Information System (INIS)
Na, Man Gyun
2001-01-01
A neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods has been developed to monitor the relevant sensor using the information of other sensors. The paramters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors
Inference of protein diffusion probed via fluorescence correlation spectroscopy
Tsekouras, Konstantinos
2015-03-01
Fluctuations are an inherent part of single molecule or few particle biophysical data sets. Traditionally, ``noise'' fluctuations have been viewed as a nuisance, to be eliminated or minimized. Here we look on how statistical inference methods - that take explicit advantage of fluctuations - have allowed us to draw an unexpected picture of single molecule diffusional dynamics. Our focus is on the diffusion of proteins probed using fluorescence correlation spectroscopy (FCS). First, we discuss how - in collaboration with the Bustamante and Marqusee labs at UC Berkeley - we determined using FCS data that individual enzymes are perturbed by self-generated catalytic heat (Riedel et al, Nature, 2014). Using the tools of inference, we found how distributions of enzyme diffusion coefficients shift in the presence of substrate revealing that enzymes performing highly exothermic reactions dissipate heat by transiently accelerating their center of mass following a catalytic reaction. Next, when molecules diffuse in the cell nucleus they often appear to diffuse anomalously. We analyze FCS data - in collaboration with Rich Day at the IU Med School - to propose a simple model for transcription factor binding-unbinding in the nucleus to show that it may give rise to apparent anomalous diffusion. Here inference methods extract entire binding affinity distributions for the diffusing transcription factors, allowing us to precisely characterize their interactions with different components of the nuclear environment. From this analysis, we draw key mechanistic insight that goes beyond what is possible by simply fitting data to ``anomalous diffusion'' models.
STRIDE: Species Tree Root Inference from Gene Duplication Events.
Emms, David M; Kelly, Steven
2017-12-01
The correct interpretation of any phylogenetic tree is dependent on that tree being correctly rooted. We present STRIDE, a fast, effective, and outgroup-free method for identification of gene duplication events and species tree root inference in large-scale molecular phylogenetic analyses. STRIDE identifies sets of well-supported in-group gene duplication events from a set of unrooted gene trees, and analyses these events to infer a probability distribution over an unrooted species tree for the location of its root. We show that STRIDE correctly identifies the root of the species tree in multiple large-scale molecular phylogenetic data sets spanning a wide range of timescales and taxonomic groups. We demonstrate that the novel probability model implemented in STRIDE can accurately represent the ambiguity in species tree root assignment for data sets where information is limited. Furthermore, application of STRIDE to outgroup-free inference of the origin of the eukaryotic tree resulted in a root probability distribution that provides additional support for leading hypotheses for the origin of the eukaryotes. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Inferring Social Influence of Anti-Tobacco Mass Media Campaign.
Zhan, Qianyi; Zhang, Jiawei; Yu, Philip S; Emery, Sherry; Xie, Junyuan
2017-07-01
Anti-tobacco mass media campaigns are designed to influence tobacco users. It has been proved that campaigns will produce users' changes in awareness, knowledge, and attitudes, and also produce meaningful behavior change of audience. Anti-smoking television advertising is the most important part in the campaign. Meanwhile, nowadays, successful online social networks are creating new media environment, however, little is known about the relation between social conversations and anti-tobacco campaigns. This paper aims to infer social influence of these campaigns, and the problem is formally referred to as the Social Influence inference of anti-Tobacco mass mEdia campaigns (Site) problem. To address the Site problem, a novel influence inference framework, TV advertising social influence estimation (Asie), is proposed based on our analysis of two real anti-tobacco campaigns. Asie divides audience attitudes toward TV ads into three distinct stages: 1) cognitive; 2) affective; and 3) conative. Audience online reactions at each of these three stages are depicted by Asie with specific probabilistic models based on the synergistic influences from both online social friends and offline TV ads. Extensive experiments demonstrate the effectiveness of Asie.
Inference of financial networks using the normalised mutual information rate
2018-01-01
In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics. PMID:29420644
Sparse linear models: Variational approximate inference and Bayesian experimental design
International Nuclear Information System (INIS)
Seeger, Matthias W
2009-01-01
A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.
Inference algorithms and learning theory for Bayesian sparse factor analysis
International Nuclear Information System (INIS)
Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John
2009-01-01
Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.
Visual recognition and inference using dynamic overcomplete sparse learning.
Murray, Joseph F; Kreutz-Delgado, Kenneth
2007-09-01
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we posit a stochastic generative world model and from it develop a simplified world model (SWM) based on a tractable variational approximation that is designed to enforce sparse coding. Recent developments in computational methods for learning overcomplete representations (Lewicki & Sejnowski, 2000; Teh, Welling, Osindero, & Hinton, 2003) suggest that overcompleteness can be useful for visual tasks, and we use an overcomplete dictionary learning algorithm (Kreutz-Delgado, et al., 2003) as a preprocessing stage to produce accurate, sparse codings of images. Inference is performed by constructing a dynamic multilayer network with feedforward, feedback, and lateral connections, which is trained to approximate the SWM. Learning is done with a variant of the back-propagation-through-time algorithm, which encourages convergence to desired states within a fixed number of iterations. Vision tasks require large networks, and to make learning efficient, we take advantage of the sparsity of each layer to update only a small subset of elements in a large weight matrix at each iteration. Experiments on a set of rotated objects demonstrate various types of visual inference and show that increasing the degree of overcompleteness improves recognition performance in difficult scenes with occluded objects in clutter.
Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.
Pecevski, Dejan; Maass, Wolfgang
2016-01-01
Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.
Social Cognition as Reinforcement Learning: Feedback Modulates Emotion Inference.
Zaki, Jamil; Kallman, Seth; Wimmer, G Elliott; Ochsner, Kevin; Shohamy, Daphna
2016-09-01
Neuroscientific studies of social cognition typically employ paradigms in which perceivers draw single-shot inferences about the internal states of strangers. Real-world social inference features much different parameters: People often encounter and learn about particular social targets (e.g., friends) over time and receive feedback about whether their inferences are correct or incorrect. Here, we examined this process and, more broadly, the intersection between social cognition and reinforcement learning. Perceivers were scanned using fMRI while repeatedly encountering three social targets who produced conflicting visual and verbal emotional cues. Perceivers guessed how targets felt and received feedback about whether they had guessed correctly. Visual cues reliably predicted one target's emotion, verbal cues predicted a second target's emotion, and neither reliably predicted the third target's emotion. Perceivers successfully used this information to update their judgments over time. Furthermore, trial-by-trial learning signals-estimated using two reinforcement learning models-tracked activity in ventral striatum and ventromedial pFC, structures associated with reinforcement learning, and regions associated with updating social impressions, including TPJ. These data suggest that learning about others' emotions, like other forms of feedback learning, relies on domain-general reinforcement mechanisms as well as domain-specific social information processing.
Sparse linear models: Variational approximate inference and Bayesian experimental design
Energy Technology Data Exchange (ETDEWEB)
Seeger, Matthias W [Saarland University and Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbruecken (Germany)
2009-12-01
A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.
Inference algorithms and learning theory for Bayesian sparse factor analysis
Energy Technology Data Exchange (ETDEWEB)
Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)
2009-12-01
Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.
Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123
Pecevski, Dejan
2016-01-01
Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214
Inferring the gene network underlying the branching of tomato inflorescence.
Directory of Open Access Journals (Sweden)
Laura Astola
Full Text Available The architecture of tomato inflorescence strongly affects flower production and subsequent crop yield. To understand the genetic activities involved, insight into the underlying network of genes that initiate and control the sympodial growth in the tomato is essential. In this paper, we show how the structure of this network can be derived from available data of the expressions of the involved genes. Our approach starts from employing biological expert knowledge to select the most probable gene candidates behind branching behavior. To find how these genes interact, we develop a stepwise procedure for computational inference of the network structure. Our data consists of expression levels from primary shoot meristems, measured at different developmental stages on three different genotypes of tomato. With the network inferred by our algorithm, we can explain the dynamics corresponding to all three genotypes simultaneously, despite their apparent dissimilarities. We also correctly predict the chronological order of expression peaks for the main hubs in the network. Based on the inferred network, using optimal experimental design criteria, we are able to suggest an informative set of experiments for further investigation of the mechanisms underlying branching behavior.
Bayesian inference for identifying interaction rules in moving animal groups.
Directory of Open Access Journals (Sweden)
Richard P Mann
Full Text Available The emergence of similar collective patterns from different self-propelled particle models of animal groups points to a restricted set of "universal" classes for these patterns. While universality is interesting, it is often the fine details of animal interactions that are of biological importance. Universality thus presents a challenge to inferring such interactions from macroscopic group dynamics since these can be consistent with many underlying interaction models. We present a Bayesian framework for learning animal interaction rules from fine scale recordings of animal movements in swarms. We apply these techniques to the inverse problem of inferring interaction rules from simulation models, showing that parameters can often be inferred from a small number of observations. Our methodology allows us to quantify our confidence in parameter fitting. For example, we show that attraction and alignment terms can be reliably estimated when animals are milling in a torus shape, while interaction radius cannot be reliably measured in such a situation. We assess the importance of rate of data collection and show how to test different models, such as topological and metric neighbourhood models. Taken together our results both inform the design of experiments on animal interactions and suggest how these data should be best analysed.
Bayesian inference of chemical kinetic models from proposed reactions
Galagali, Nikhil
2015-02-01
© 2014 Elsevier Ltd. Bayesian inference provides a natural framework for combining experimental data with prior knowledge to develop chemical kinetic models and quantify the associated uncertainties, not only in parameter values but also in model structure. Most existing applications of Bayesian model selection methods to chemical kinetics have been limited to comparisons among a small set of models, however. The significant computational cost of evaluating posterior model probabilities renders traditional Bayesian methods infeasible when the model space becomes large. We present a new framework for tractable Bayesian model inference and uncertainty quantification using a large number of systematically generated model hypotheses. The approach involves imposing point-mass mixture priors over rate constants and exploring the resulting posterior distribution using an adaptive Markov chain Monte Carlo method. The posterior samples are used to identify plausible models, to quantify rate constant uncertainties, and to extract key diagnostic information about model structure-such as the reactions and operating pathways most strongly supported by the data. We provide numerical demonstrations of the proposed framework by inferring kinetic models for catalytic steam and dry reforming of methane using available experimental data.
Inference with minimal Gibbs free energy in information field theory
International Nuclear Information System (INIS)
Ensslin, Torsten A.; Weig, Cornelius
2010-01-01
Non-linear and non-Gaussian signal inference problems are difficult to tackle. Renormalization techniques permit us to construct good estimators for the posterior signal mean within information field theory (IFT), but the approximations and assumptions made are not very obvious. Here we introduce the simple concept of minimal Gibbs free energy to IFT, and show that previous renormalization results emerge naturally. They can be understood as being the Gaussian approximation to the full posterior probability, which has maximal cross information with it. We derive optimized estimators for three applications, to illustrate the usage of the framework: (i) reconstruction of a log-normal signal from Poissonian data with background counts and point spread function, as it is needed for gamma ray astronomy and for cosmography using photometric galaxy redshifts, (ii) inference of a Gaussian signal with unknown spectrum, and (iii) inference of a Poissonian log-normal signal with unknown spectrum, the combination of (i) and (ii). Finally we explain how Gaussian knowledge states constructed by the minimal Gibbs free energy principle at different temperatures can be combined into a more accurate surrogate of the non-Gaussian posterior.
Bayesian inference on genetic merit under uncertain paternity
Directory of Open Access Journals (Sweden)
Tempelman Robert J
2003-09-01
Full Text Available Abstract A hierarchical animal model was developed for inference on genetic merit of livestock with uncertain paternity. Fully conditional posterior distributions for fixed and genetic effects, variance components, sire assignments and their probabilities are derived to facilitate a Bayesian inference strategy using MCMC methods. We compared this model to a model based on the Henderson average numerator relationship (ANRM in a simulation study with 10 replicated datasets generated for each of two traits. Trait 1 had a medium heritability (h2 for each of direct and maternal genetic effects whereas Trait 2 had a high h2 attributable only to direct effects. The average posterior probabilities inferred on the true sire were between 1 and 10% larger than the corresponding priors (the inverse of the number of candidate sires in a mating pasture for Trait 1 and between 4 and 13% larger than the corresponding priors for Trait 2. The predicted additive and maternal genetic effects were very similar using both models; however, model choice criteria (Pseudo Bayes Factor and Deviance Information Criterion decisively favored the proposed hierarchical model over the ANRM model.
A unified framework for haplotype inference in nuclear families.
Iliadis, Alexandros; Anastassiou, Dimitris; Wang, Xiaodong
2012-07-01
Many large genome-wide association studies include nuclear families with more than one child (trio families), allowing for analysis of differences between siblings (sib pair analysis). Statistical power can be increased when haplotypes are used instead of genotypes. Currently, haplotype inference in families with more than one child can be performed either using the familial information or statistical information derived from the population samples but not both. Building on our recently proposed tree-based deterministic framework (TDS) for trio families, we augment its applicability to general nuclear families. We impose a minimum recombinant approach locally and independently on each multiple children family, while resorting to the population-derived information to solve the remaining ambiguities. Thus our framework incorporates all available information (familial and population) in a given study. We demonstrate that using all the constraints in our approach we can have gains in the accuracy as opposed to breaking the multiple children families to separate trios and resorting to a trio inference algorithm or phasing each family in isolation. We believe that our proposed framework could be the method of choice for haplotype inference in studies that include nuclear families with multiple children. Our software (tds2.0) is downloadable from www.ee.columbia.edu/∼anastas/tds. © 2012 The Authors Annals of Human Genetics © 2012 Blackwell Publishing Ltd/University College London.
Evidence Accumulation and Change Rate Inference in Dynamic Environments.
Radillo, Adrian E; Veliz-Cuba, Alan; Josić, Krešimir; Kilpatrick, Zachary P
2017-06-01
In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible change point counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation-based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments and map this computation to reduced models that perform inference using plausible neural mechanisms.
Likelihood-Based Inference of B Cell Clonal Families.
Directory of Open Access Journals (Sweden)
Duncan K Ralph
2016-10-01
Full Text Available The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called "rearrangement" forming progenitor B cells, then a Darwinian process of lineage diversification and selection called "affinity maturation." The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem "clonal family inference." In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets.
Probabilistic Inference of Biological Networks via Data Integration
Directory of Open Access Journals (Sweden)
Mark F. Rogers
2015-01-01
Full Text Available There is significant interest in inferring the structure of subcellular networks of interaction. Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links. Many types of data are relevant to inferring functional links between genes, motivating the use of data integration. We use pairwise kernels to predict novel links, along with multiple kernel learning to integrate distinct sources of data into a decision function. We evaluate various pairwise kernels to establish which are most informative and compare individual kernel accuracies with accuracies for weighted combinations. By associating a probability measure with classifier predictions, we enable cautious classification, which can increase accuracy by restricting predictions to high-confidence instances, and data cleaning that can mitigate the influence of mislabeled training instances. Although one pairwise kernel (the tensor product pairwise kernel appears to work best, different kernels may contribute complimentary information about interactions: experiments in S. cerevisiae (yeast reveal that a weighted combination of pairwise kernels applied to different types of data yields the highest predictive accuracy. Combined with cautious classification and data cleaning, we can achieve predictive accuracies of up to 99.6%.
Inference of financial networks using the normalised mutual information rate.
Goh, Yong Kheng; Hasim, Haslifah M; Antonopoulos, Chris G
2018-01-01
In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics.
Causal Inference and Explaining Away in a Spiking Network
Moreno-Bote, Rubén; Drugowitsch, Jan
2015-01-01
While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426
STRATEGIES IN SEISMIC INFERENCE OF SUPERGRANULAR FLOWS ON THE SUN
Energy Technology Data Exchange (ETDEWEB)
Bhattacharya, Jishnu; Hanasoge, Shravan M. [Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Mumbai-400005 (India)
2016-08-01
Observations of the solar surface reveal the presence of flows with length scales of around 35 Mm, commonly referred to as supergranules. Inferring the subsurface flow profile of supergranules from measurements of the surface and photospheric wavefield is an important challenge faced by helioseismology. Traditionally, the inverse problem has been approached by studying the linear response of seismic waves in a horizontally translationally invariant background to the presence of the supergranule; following an iterative approach that does not depend on horizontal translational invariance might perform better, since the misfit can be analyzed post iterations. In this work, we construct synthetic observations using a reference supergranule and invert for the flow profile using surface measurements of travel times of waves belonging to modal ridges f (surface gravity) and p {sub 1} through p {sub 7} (acoustic). We study the extent to which individual modes and their combinations contribute to infer the flow. We show that this method of nonlinear iterative inversion tends to underestimate the flow velocities, as well as inferring a shallower flow profile, with significant deviations from the reference supergranule near the surface. We carry out a similar analysis for a sound-speed perturbation and find that analogous near-surface deviations persist, although the iterations converge faster and more accurately. We conclude that a better approach to inversion would be to expand the supergranule profile in an appropriate basis, thereby reducing the number of parameters being inverted for and appropriately regularizing them.
Generating inferences from knowledge structures based on general automata
Energy Technology Data Exchange (ETDEWEB)
Koenig, E C
1983-01-01
The author shows that the model for knowledge structures for computers based on general automata accommodates procedures for establishing inferences. Algorithms are presented which generate inferences as output of a computer when its sentence input names appropriate knowledge elements contained in an associated knowledge structure already stored in the memory of the computer. The inferences are found to have either a single graph tuple or more than one graph tuple of associated knowledge. Six algorithms pertain to a single graph tuple and a seventh pertains to more than one graph tuple of associated knowledge. A named term is either the automaton, environment, auxiliary receptor, principal receptor, auxiliary effector, or principal effector. The algorithm pertaining to more than one graph tuple requires that the input sentence names the automaton, transformation response, and environment of one of the tuples of associated knowledge in a sequence of tuples. Interaction with the computer may be either in a conversation or examination mode. The algorithms are illustrated by an example. 13 references.
A combinatorial perspective of the protein inference problem.
Yang, Chao; He, Zengyou; Yu, Weichuan
2013-01-01
In a shotgun proteomics experiment, proteins are the most biologically meaningful output. The success of proteomics studies depends on the ability to accurately and efficiently identify proteins. Many methods have been proposed to facilitate the identification of proteins from peptide identification results. However, the relationship between protein identification and peptide identification has not been thoroughly explained before. In this paper, we devote ourselves to a combinatorial perspective of the protein inference problem. We employ combinatorial mathematics to calculate the conditional protein probabilities (protein probability means the probability that a protein is correctly identified) under three assumptions, which lead to a lower bound, an upper bound, and an empirical estimation of protein probabilities, respectively. The combinatorial perspective enables us to obtain an analytical expression for protein inference. Our method achieves comparable results with ProteinProphet in a more efficient manner in experiments on two data sets of standard protein mixtures and two data sets of real samples. Based on our model, we study the impact of unique peptides and degenerate peptides (degenerate peptides are peptides shared by at least two proteins) on protein probabilities. Meanwhile, we also study the relationship between our model and ProteinProphet. We name our program ProteinInfer. Its Java source code, our supplementary document and experimental results are available at: >http://bioinformatics.ust.hk/proteininfer.
Matrix dimensions bias demographic inferences: implications for comparative plant demography.
Salguero-Gómez, Roberto; Plotkin, Joshua B
2010-12-01
While the wealth of projection matrices in plant demography permits comparative studies, variation in matrix dimensions complicates interspecific comparisons. Collapsing matrices to a common dimension may facilitate such comparisons but may also bias the inferred demographic parameters. Here we examine how matrix dimension affects inferred demographic elasticities and how different collapsing criteria perform. We analyzed 13 x 13 matrices representing nine plant species, collapsing these matrices (i) into even 7 x 7, 5 x 5, 4 x 4, and 3 x 3 matrices and (ii) into 5 x 5 matrices using different criteria. Stasis and fecundity elasticities increased when matrix dimension was reduced, whereas those of progression and retrogression decreased. We suggest a collapsing criterion that minimizes dissimilarities between the original- and collapsed-matrix elasticities and apply it to 66 plant species to study how life span and growth form influence the relationship between matrix dimension and elasticities. Our analysis demonstrates that (i) projection matrix dimension has significant effects on inferred demographic parameters, (ii) there are better-performing methods than previously suggested for standardizing matrix dimension, and (iii) herbaceous perennial projection matrices are particularly sensitive to changes in matrix dimensionality. For comparative demographic studies, we recommend normalizing matrices to a common dimension by collapsing higher classes and leaving the first few classes unaltered.
Bootstrap-based Support of HGT Inferred by Maximum Parsimony
Directory of Open Access Journals (Sweden)
Nakhleh Luay
2010-05-01
Full Text Available Abstract Background Maximum parsimony is one of the most commonly used criteria for reconstructing phylogenetic trees. Recently, Nakhleh and co-workers extended this criterion to enable reconstruction of phylogenetic networks, and demonstrated its application to detecting reticulate evolutionary relationships. However, one of the major problems with this extension has been that it favors more complex evolutionary relationships over simpler ones, thus having the potential for overestimating the amount of reticulation in the data. An ad hoc solution to this problem that has been used entails inspecting the improvement in the parsimony length as more reticulation events are added to the model, and stopping when the improvement is below a certain threshold. Results In this paper, we address this problem in a more systematic way, by proposing a nonparametric bootstrap-based measure of support of inferred reticulation events, and using it to determine the number of those events, as well as their placements. A number of samples is generated from the given sequence alignment, and reticulation events are inferred based on each sample. Finally, the support of each reticulation event is quantified based on the inferences made over all samples. Conclusions We have implemented our method in the NEPAL software tool (available publicly at http://bioinfo.cs.rice.edu/, and studied its performance on both biological and simulated data sets. While our studies show very promising results, they also highlight issues that are inherently challenging when applying the maximum parsimony criterion to detect reticulate evolution.
Bootstrap-based support of HGT inferred by maximum parsimony.
Park, Hyun Jung; Jin, Guohua; Nakhleh, Luay
2010-05-05
Maximum parsimony is one of the most commonly used criteria for reconstructing phylogenetic trees. Recently, Nakhleh and co-workers extended this criterion to enable reconstruction of phylogenetic networks, and demonstrated its application to detecting reticulate evolutionary relationships. However, one of the major problems with this extension has been that it favors more complex evolutionary relationships over simpler ones, thus having the potential for overestimating the amount of reticulation in the data. An ad hoc solution to this problem that has been used entails inspecting the improvement in the parsimony length as more reticulation events are added to the model, and stopping when the improvement is below a certain threshold. In this paper, we address this problem in a more systematic way, by proposing a nonparametric bootstrap-based measure of support of inferred reticulation events, and using it to determine the number of those events, as well as their placements. A number of samples is generated from the given sequence alignment, and reticulation events are inferred based on each sample. Finally, the support of each reticulation event is quantified based on the inferences made over all samples. We have implemented our method in the NEPAL software tool (available publicly at http://bioinfo.cs.rice.edu/), and studied its performance on both biological and simulated data sets. While our studies show very promising results, they also highlight issues that are inherently challenging when applying the maximum parsimony criterion to detect reticulate evolution.
Prediksi Kelulusan Mata Kuliah Menggunakan Hybrid Fuzzy Inference System
Directory of Open Access Journals (Sweden)
Abidatul Izzah
2016-07-01
Full Text Available AbstrakPerguruan Tinggi merupakan salah satu institusi yang menyimpan data yang sangat informatif jika diolah secara baik. Prediksi kelulusan mahasiswa merupakan kasus di Perguruan Tinggi yang cukup banyak diteliti. Dengan mengetahui prediksi status kelulusan mahasiswa di tengah semester, dosen dapat mengantisipasi atau memberi perhatian khusus pada siswa yang diprediksi tidak lulus. Metode yang digunakan sangat bervariatif termasuk metode Fuzzy Inference System (FIS. Namun dalam implementasinya, proses pembangkitan rule fuzzy sering dilakukan secara random atau berdasarkan pemahaman pakar sehingga tidak merepresentasikan sebaran data. Oleh karena itu, dalam penelitian ini digunakan teknik Decision Tree (DT untuk membangkitkan rule. Dari uraian tersebut, penelitian bertujuan untuk memprediksi kelulusan mata kuliah menggunakan hybrid FIS dan DT. Data yang digunakan dalam penelitian ini adalah data nilai Posttest, Tugas, Kuis, dan UTS dari 106 mahasiswa Politeknik Kediri pengikut mata kuliah Algoritma dan Struktur Data. Penelitian ini diawali dari membangkitkan 5 rule yang selanjutnya digunakan dalam inferensi. Tahap selanjutnya adalah implementasi FIS dengan tahapan fuzzifikasi, inferensi, dan defuzzifikasi. Hasil yang diperoleh adalah akurasi, sensitivitas, dan spesifisitas masing-masing adalah 94.33%, 96.55%, dan 84.21%.Kata kunci: Decision Tree, Educational Data Mining, Fuzzy Inference System, Prediksi. AbstractCollege is an institution that holds very informative data if it mined properly. Prediction about student’s graduation is a common case that many discussed. Having the predictions of student’s graduation in the middle semester, lecturer will anticipate or give some special attention to students who would be not passed. The method used to prediction is very varied including Fuzzy Inference System (FIS. However, fuzzy rule process is often generated randomly or based on knowledge experts that not represent the data distribution
Packaging design as communicator of product attributes: Effects on consumers’ attribute inferences
van Ooijen, I.
2016-01-01
This dissertation will focus on two types of attribute inferences that result from packaging design cues. First, the effects of product packaging design on quality related inferences are investigated. Second, the effects of product packaging design on healthiness related inferences are examined (See
Online Emotional Inferences in Written and Auditory Texts: A Study with Children and Adults
Diergarten, Anna Katharina; Nieding, Gerhild
2016-01-01
Emotional inferences are conclusions that a reader draws about the emotional state of a story's protagonist. In this study, we examined whether children and adults draw emotional inferences while reading short stories or listening to an aural presentation of short stories. We used an online method that assesses inferences during reading with a…
Assessment of network inference methods: how to cope with an underdetermined problem.
Directory of Open Access Journals (Sweden)
Caroline Siegenthaler
Full Text Available The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.
Evaluated cross section libraries
International Nuclear Information System (INIS)
Maqurno, B.A.
1976-01-01
The dosimetry tape (ENDF/B-IV tape 412) was issued in a general CSEWG distribution, August 1974. The pointwise cross section data file was tested with specified reference spectra. A group averaged cross section data file (620 groups based on tape 412) was tested with the above spectra and the results are presented in this report
International Nuclear Information System (INIS)
Del Duca, V.
1992-11-01
Minijet production in jet inclusive cross sections at hadron colliders, with large rapidity intervals between the tagged jets, is evaluated by using the BFKL pomeron. We describe the jet inclusive cross section for an arbitrary number of tagged jets, and show that it behaves like a system of coupled pomerons
Supervised learning for infection risk inference using pathology data.
Hernandez, Bernard; Herrero, Pau; Rawson, Timothy Miles; Moore, Luke S P; Evans, Benjamin; Toumazou, Christofer; Holmes, Alison H; Georgiou, Pantelis
2017-12-08
Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrobial selection advice. However, bypassing an initial assessment to determine the existence of an underlying disease that justifies the need of antimicrobial therapy might lead to indiscriminate and often unnecessary prescriptions. From pathology laboratory tests, six biochemical markers were selected and combined with microbiology outcomes from susceptibility tests to create a unique dataset with over one and a half million daily profiles to perform infection risk inference. Outliers were discarded using the inter-quartile range rule and several sampling techniques were studied to tackle the class imbalance problem. The first phase selects the most effective and robust model during training using ten-fold stratified cross-validation. The second phase evaluates the final model after isotonic calibration in scenarios with missing inputs and imbalanced class distributions. More than 50% of infected profiles have daily requested laboratory tests for the six biochemical markers with very promising infection inference results: area under the receiver operating characteristic curve (0.80-0.83), sensitivity (0.64-0.75) and specificity (0.92-0.97). Standardization consistently outperforms normalization and sensitivity is enhanced by using the SMOTE sampling technique. Furthermore, models operated without noticeable loss in performance if at least four biomarkers were available. The selected biomarkers comprise enough information to perform infection risk inference with a high degree of confidence even in the presence of incomplete and imbalanced data. Since they are commonly available in hospitals, Clinical Decision Support Systems could
A comparison of ROC inferred from FROC and conventional ROC
McEntee, Mark F.; Littlefair, Stephen; Pietrzyk, Mariusz W.
2014-03-01
This study aims to determine whether receiver operating characteristic (ROC) scores inferred from free-response receiver operating characteristic (FROC) were equivalent to conventional ROC scores for the same readers and cases. Forty-five examining radiologists of the American Board of Radiology independently reviewed 47 PA chest radiographs under at least two conditions. Thirty-seven cases had abnormal findings and 10 cases had normal findings. Half the readers were asked to first locate any visualized lung nodules, mark them and assign a level of confidence [the FROC mark-rating pair] and second give an overall to the entire image on the same scale [the ROC score]. The second half of readers gave the ROC rating first followed by the FROC mark-rating pairs. A normal image was represented with number 1 and malignant lesions with numbers 2-5. A jackknife free-response receiver operating characteristic (JAFROC), and inferred ROC (infROC) was calculated from the mark-rating pairs using JAFROC V4.1 software. ROC based on the overall rating of the image calculated using DBM MRMC software, which was also used to compare infROC and ROC AUCs treating the methods as modalities. Pearson's correlations coefficient and linear regression were used to examine their relationship using SPSS, version 21.0; (SPSS, Chicago, IL). The results of this study showed no significant difference between the ROC and Inferred ROC AUCs (p≤0.25). While Pearson's correlation coefficient was 0.7 (p≤0.01). Inter-reader correlation calculated from Obuchowski- Rockette covariance's ranged from 0.43-0.86 while intra-reader agreement was greater than previously reported ranging from 0.68-0.82.
Network inference from functional experimental data (Conference Presentation)
Desrosiers, Patrick; Labrecque, Simon; Tremblay, Maxime; Bélanger, Mathieu; De Dorlodot, Bertrand; Côté, Daniel C.
2016-03-01
Functional connectivity maps of neuronal networks are critical tools to understand how neurons form circuits, how information is encoded and processed by neurons, how memory is shaped, and how these basic processes are altered under pathological conditions. Current light microscopy allows to observe calcium or electrical activity of thousands of neurons simultaneously, yet assessing comprehensive connectivity maps directly from such data remains a non-trivial analytical task. There exist simple statistical methods, such as cross-correlation and Granger causality, but they only detect linear interactions between neurons. Other more involved inference methods inspired by information theory, such as mutual information and transfer entropy, identify more accurately connections between neurons but also require more computational resources. We carried out a comparative study of common connectivity inference methods. The relative accuracy and computational cost of each method was determined via simulated fluorescence traces generated with realistic computational models of interacting neurons in networks of different topologies (clustered or non-clustered) and sizes (10-1000 neurons). To bridge the computational and experimental works, we observed the intracellular calcium activity of live hippocampal neuronal cultures infected with the fluorescent calcium marker GCaMP6f. The spontaneous activity of the networks, consisting of 50-100 neurons per field of view, was recorded from 20 to 50 Hz on a microscope controlled by a homemade software. We implemented all connectivity inference methods in the software, which rapidly loads calcium fluorescence movies, segments the images, extracts the fluorescence traces, and assesses the functional connections (with strengths and directions) between each pair of neurons. We used this software to assess, in real time, the functional connectivity from real calcium imaging data in basal conditions, under plasticity protocols, and epileptic