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Books on the topic 'Bayesian framework'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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1

Stübler, Sabine. Modelling Proteasome Dynamics in a Bayesian Framework. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-20167-8.

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2

Bazaldua, Diego A. Luna. Exploring Skill Condensation Rules for Cognitive Diagnostic Models in a Bayesian Framework. [New York, N.Y.?]: [publisher not identified], 2015.

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3

Chung, Meng-ta. Estimating the Q-matrix for Cognitive Diagnosis Models in a Bayesian Framework. [New York, N.Y.?]: [publisher not identified], 2014.

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4

Stübler, Sabine. Modelling Proteasome Dynamics in a Bayesian Framework. Springer Spektrum, 2017.

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5

Titelbaum, Michael G. Quitting Certainties: A Bayesian Framework Modeling Degrees of Belief. Oxford University Press, 2014.

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6

Quitting Certainties A Bayesian Framework Modeling Degrees Of Belief. Oxford University Press, USA, 2013.

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7

Titelbaum, Michael G. Quitting Certainties: A Bayesian Framework Modeling Degrees of Belief. Oxford University Press, Incorporated, 2012.

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8

Korrapati, Raghu B. A Bayesian Model Framework to Determine Patient Compliance in Glaucoma Cases. iUniverse, Inc., 2005.

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9

Yu, Angela J. Bayesian Models of Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.025.

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Traditionally, attentional selection has been thought of as arising naturally from resource limitations, with a focus on what might be the most apt metaphor, e.g. whether it is a ‘bottleneck’ or ‘spotlight’. However, these simple metaphors cannot account for the specificity, flexibility, and heterogeneity of the way attentional selection manifests itself in different behavioural contexts. A recent body of theoretical work has taken a different approach, focusing on the computational needs of selective processing, relative to environmental constraints and behavioural goals. They typically adopt a normative computational framework, incorporating Bayes-optimal algorithms for information processing and action selection. This chapter reviews some of this recent modelling work, specifically in the context of attention for learning, covert spatial attention, and overt spatial attention.
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10

Pearl, Lisa, and Sharon Goldwater. Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition. Edited by Jeffrey L. Lidz, William Snyder, and Joe Pater. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199601264.013.28.

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Bayesian models of language acquisition are powerful tools for exploring how linguistic generalizations can be made. Notably, Bayesian models assume children leverage statistical information in sophisticated ways, and so it is important to demonstrate that children’s behavior is consistent with both the assumptions of the Bayesian framework and the predictions of specific models. We first provide a historical overview of behavioral evidence suggesting children utilize available statistical information to make useful generalizations in a variety of tasks. We then discuss the Bayesian modeling framework, including benefits of particular interest to both developmental and theoretical linguists. We conclude with a review of several case studies that demonstrate how Bayesian models can be applied to problems of interest in language acquisition.
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11

Austerweil, Joseph L., Samuel J. Gershman, and Thomas L. Griffiths. Structure and Flexibility in Bayesian Models of Cognition. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.9.

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Probability theory forms a natural framework for explaining the impressive success of people at solving many difficult inductive problems, such as learning words and categories, inferring the relevant features of objects, and identifying functional relationships. Probabilistic models of cognition use Bayes’s rule to identify probable structures or representations that could have generated a set of observations, whether the observations are sensory input or the output of other psychological processes. In this chapter we address an important question that arises within this framework: How do people infer representations that are complex enough to faithfully encode the world but not so complex that they “overfit” noise in the data? We discuss nonparametric Bayesian models as a potential answer to this question. To do so, first we present the mathematical background necessary to understand nonparametric Bayesian models. We then delve into nonparametric Bayesian models for three types of hidden structure: clusters, features, and functions. Finally, we conclude with a summary and discussion of open questions for future research.
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12

Poston, Ted. The Argument from (A) to (Y). Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190842215.003.0023.

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This chapter provides a Bayesian model of strength of evidence in cases in which there are multiple items of independent evidence. The author uses this Bayesian model to evaluate the strength of evidence for theism if, as Plantinga claims, there are two dozen or so arguments for theism. The model turns questions of the overall strength of multiple arguments into a simple summation problem. Moreover, it provides a clear framework for advancing questions about how relationships between the arguments bear on the overall strength of evidence for theism. The Bayesian model developed in this chapter has a wide-range of applications for modeling strength of evidence in cumulative case arguments.
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13

Davies, Martin, and Andy Egan. Delusion. Edited by K. W. M. Fulford, Martin Davies, Richard G. T. Gipps, George Graham, John Z. Sadler, Giovanni Stanghellini, and Tim Thornton. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199579563.013.0042.

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Cognitive approaches contribute to our understanding of delusions by providing an explanatory framework that extends beyond the personal level to the sub personal level of information-processing systems. According to one influential cognitive approach, two factors are required to account for the content of a delusion, its initial adoption as a belief, and its persistence. This chapter reviews Bayesian developments of the two-factor framework.
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14

Lopes, Hedibert, and Nicholas Polson. Analysis of economic data with multiscale spatio-temporal models. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.12.

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This article discusses the use of Bayesian multiscale spatio-temporal models for the analysis of economic data. It demonstrates the utility of a general modelling approach for multiscale analysis of spatio-temporal processes with areal data observations in an economic study of agricultural production in the Brazilian state of Espìrito Santo during the period 1990–2005. The article first describes multiscale factorizations for spatial processes before presenting an exploratory multiscale data analysis and explaining the motivation for multiscale spatio-temporal models. It then examines the temporal evolution of the underlying latent multiscale coefficients and goes on to introduce a Bayesian analysis based on the multiscale decomposition of the likelihood function along with Markov chain Monte Carlo (MCMC) methods. The results from agricultural production analysis show that the spatio-temporal framework can effectively analyse massive economics data sets.
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15

Golan, Amos. Info-Metrics and Statistical Inference. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0013.

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In this chapter I concentrate on continuous inferential problems: problems where the dependent variable is continuous, such as classical regression problems. As in the previous chapter, using duality theory, I show that the info-metrics framework is general enough to include the class of information-theoretic methods as a special case. The formulation is developed for the classical regression problem, but the results apply to many other problems. A detailed discussion of the benefits and costs of using the info-metrics framework is provided and contrasted with other approaches. I use theoretical examples and policy-relevant applications to demonstrate the method. The common problem of misspecification is also discussed and studied within the info-metrics framework. I show that a misspecified model and a correctly specified one can yield similar answers. The appendices provide detailed discussions of the generalized method of moments and the Bayesian method of moments. Both are connected to info-metrics.
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16

Friston, Karl J., and Raymond J. Dolan. Computational Psychiatry and the Bayesian Brain. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0072.

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This chapter considers recent advances in computational neuroscience that are especially relevant for psychiatry. We offer a review of computational psychiatry in terms of its ambitions, emerging domains of application, and promises for the future. Our focus is on theoretical formulations of brain function that accommodate subjective beliefs and behavior within formal (computational) frameworks—frameworks that can be grounded in neurophysiology down to the level of synaptic mechanisms. Understanding the nature and principles that underlie functional brain architectures is, we assume, essential for understanding and phenotyping psychopathology and its pathophysiological underpinnings. To illustrate computational approaches to psychiatric disorders, we focus on active (Bayesian) inference and predictive coding. Specifically, we try to explain how the basic principles of neuronal computation are being used to understand psychiatric phenomena, ranging from affiliative behavior and theory of mind in autism to abnormalities of smooth pursuit eye movements in schizophrenia.
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17

Griffiths, Thomas L. Formalizing Prior Knowledge in Causal Induction. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.38.

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Prior knowledge plays a central role in causal induction, helping to explain how people are capable of identifying causal relationships from small amounts of data. Bayesian inference provides a way to characterize the influence that prior knowledge should have on causal induction, as well as an explanation for how that knowledge could itself be acquired. Using the theory-based causal induction framework of Griffiths and Tenenbaum (2009), this chapter reviews recent work exploring the relationship between prior knowledge and causal induction, highlighting some of the ways in which people’s expectations about causal relationships differ from approaches to causal learning in statistics and computer science.
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18

von Mohr, Mariana, and Aikaterini Fotopoulou. The cutaneous borders of interoception: Active and social inference of pain and pleasure on the skin. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198811930.003.0006.

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Pain and pleasant touch have been recently classified as interoceptive modalities. This reclassification lies at the heart of long-standing debates questioning whether these modalities should be defined as sensations on their basis of neurophysiological specificity at the periphery or as homeostatic emotions on the basis of top-down convergence and modulation at the spinal and brain levels. Here, we outline the literature on the peripheral and central neurophysiology of pain and pleasant touch. We next recast this literature within a recent Bayesian predictive coding framework, namely active inference. This recasting puts forward a unifying model of bottom-up and top-down determinants of pain and pleasant touch and the role of social factors in modulating the salience of peripheral signals reaching the brain.
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19

Tebaldi, Claudia, and Richard Smith. Indirect elicitation from ecological experts: From methods and software to habitat modelling and rock-wallabies. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.19.

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This article focuses on techniques for eliciting expert judgement about complex uncertainties, and more specifically the habitat of the Australian brush-tailed rock-wallaby. Modelling wildlife habitat requirements is important for mapping the distribution of the rock-wallaby, a threatened species, and therefore informing conservation and management. The Bayesian statistical modelling framework provides a useful ‘bridge’, from purely expert-defined models, to statistical models allowing survey data and expert knowledge to be ‘viewed as complementary, rather than alternative or competing, information sources’. The article describes the use of a rigorously designed and implemented expert elicitation for multiple experts, as well as a software tool for streamlining, automating and facilitating an indirect approach to elicitation. This approach makes it possible to infer the relationship between probability of occurrence and the environmental variables and demonstrates how expert knowledge can contribute to habitat modelling.
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20

Comesaña, Juan. Being Rational and Being Right. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198847717.001.0001.

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This book defends a cluster of theses related to the rationality of action and belief. The starting point is that rational action requires rational belief but tolerates false belief. From there, it argues for a novel account of empirical evidence according to which said evidence consists of the content of undefeated experiences. This view, “Experientialism,” differs from the two main views of empirical evidence on offer nowadays: Factualism, according to which our evidence is what we know, and Psychologism, according to which our experiences themselves are evidence. The book argues that Experientialism fares better than these rival views in explaining different features of rational belief and action. The discussion is embedded in a Bayesian framework, and the book also examines the problem of normative requirements, the easy knowledge problem, and how Experientialism compares to Evidentialism, Reliabilism, and Comesaña’s own (now superseded) Evidentialist Reliabilism.
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21

Higdon, Dave, Katrin Heitmann, Charles Nakhleh, and Salman Habib. Combining simulations and physical observations to estimate cosmological parameters. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.26.

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This article focuses on the use of a Bayesian approach that combines simulations and physical observations to estimate cosmological parameters. It begins with an overview of the Λ-cold dark matter (CDM) model, the simplest cosmological model in agreement with the cosmic microwave background (CMB) and largescale structure analysis. The CDM model is determined by a small number of parameters which control the composition, expansion and fluctuations of the universe. The present study aims to learn about the values of these parameters using measurements from the Sloan Digital Sky Survey (SDSS). Computationally intensive simulation results are combined with measurements from the SDSS to infer about a subset of the parameters that control the CDM model. The article also describes a statistical framework used to determine a posterior distribution for these cosmological parameters and concludes by showing how it can be extended to include data from diverse data sources.
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22

Liang, Percy, Michael Jordan, and Dan Klein. Probabilistic grammars and hierarchical Dirichlet processes. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.27.

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This article focuses on the use of probabilistic context-free grammars (PCFGs) in natural language processing involving a large-scale natural language parsing task. It describes detailed, highly-structured Bayesian modelling in which model dimension and complexity responds naturally to observed data. The framework, termed hierarchical Dirichlet process probabilistic context-free grammar (HDP-PCFG), involves structured hierarchical Dirichlet process modelling and customized model fitting via variational methods to address the problem of syntactic parsing and the underlying problems of grammar induction and grammar refinement. The central object of study is the parse tree, which can be used to describe a substantial amount of the syntactic structure and relational semantics of natural language sentences. The article first provides an overview of the formal probabilistic specification of the HDP-PCFG, algorithms for posterior inference under the HDP-PCFG, and experiments on grammar learning run on the Wall Street Journal portion of the Penn Treebank.
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23

Chen, Min, J. Michael Dunn, Amos Golan, and Aman Ullah, eds. Advances in Info-Metrics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190636685.001.0001.

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Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. In a recent book on the Foundations of Info-Metrics, Golan (OUP, 2018) provides the theoretical underpinning of info-metrics and the necessary tools and building blocks for using that framework. This volume complements Golan’s book and expands on the series of studies on the classical maximum entropy and Bayesian methods published in the different proceedings started with the seminal collection of Levine and Tribus (1979) and continuing annually. The objective of this volume is to expand the study of info-metrics, and information processing, across the sciences and to further explore the basis of information-theoretic inference and its mathematical and philosophical foundations. This volume is inherently interdisciplinary and applications oriented. It contains some of the recent developments in the field, as well as many new cross-disciplinary case studies and examples. The emphasis here is on the interrelationship between information and inference where we view the word ‘inference’ in its most general meaning – capturing all types of problem solving. That includes model building, theory creation, estimation, prediction, and decision making. The volume contains nineteen chapters in seven parts. Although chapters in each part are related, each chapter is self-contained; it provides the necessary tools for using the info-metrics framework for solving the problem confronted in that chapter. This volume is designed to be accessible for researchers, graduate students, and practitioners across the disciplines, requiring only some basic quantitative skills. The multidisciplinary nature and applications provide a hands-on experience for the reader.
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24

Dhami, Sanjit, and Cass R. Sunstein. Bounded Rationality. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/14401.001.0001.

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Two leaders in the field explore the foundations of bounded rationality and its effects on choices by individuals, firms, and the government. Bounded rationality recognizes that human behavior departs from the perfect rationality assumed by neoclassical economics. In this book, Sanjit Dhami and Cass R. Sunstein explore the foundations of bounded rationality and consider the implications of this approach for public policy and law, in particular for questions about choice, welfare, and freedom. The authors, both recognized as experts in the field, cover a wide range of empirical findings and assess theoretical work that attempts to explain those findings. Their presentation is comprehensive, coherent, and lucid, with even the most technical material explained accessibly. They not only offer observations and commentary on the existing literature but also explore new insights, ideas, and connections. After examining the traditional neoclassical framework, which they refer to as the Bayesian rationality approach (BRA), and its empirical issues, Dhami and Sunstein offer a detailed account of bounded rationality and how it can be incorporated into the social and behavioral sciences. They also discuss a set of models of heuristics-based choice and the philosophical foundations of behavioral economics. Finally, they examine libertarian paternalism and its strategies of “nudges.”
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25

Swiney, Lauren. Activity, Agency, and Inner Speech Pathology. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198796640.003.0013.

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Over the last thirty years the comparator hypothesis has emerged as a prominent account of inner speech pathology. This chapter discusses a number of cognitive accounts broadly derived from this approach, highlighting the existence of two importantly distinct notions of inner speech in the literature; one as a prediction in the absence of sensory input, the other as an act with sensory consequences that are themselves predicted. Under earlier frameworks in which inner speech is described in the context of classic models of motor control, I argue that these two notions may be compatible, providing two routes to inner speech pathology. Under more recent accounts grounded in the architecture of Bayesian predictive processing, I argue that “active inference” approaches to action generation pose serious challenges to the plausibility of the latter notion of inner speech, while providing the former notion with rich explanatory possibilities for inner speech pathology.
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