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Books on the topic 'Bayesian belief'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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1

Marshall, Adele Heather. Bayesian belief networks using conditional phase-type distibutions. [s.l: The Author], 2001.

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2

Pershad, Rinku. A Bayesian belief network for corporate credit risk assessment. Ottawa: National Library of Canada, 2000.

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3

Brian S. G. E. Sahely. Development of a bayesian belief network for anaerobic wastewater treatment. Ottawa: National Library of Canada, 2000.

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4

Social capital modeling in virtual communities: Bayesian belief network approaches. Hershey, PA: Information Science Reference, 2009.

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5

Epstein, Larry G. Dynamically consistent beliefs must be Bayesian. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1992.

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6

Feldman, Mark. On the generic nonconvergence of Bayesian actions and beliefs. Urbana, Ill: College of Commerce and Business Administration, Bureau of Economic and Business Research, University of Illinois at Urbana-Champaign, 1990.

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7

Feldman, Mark. On the generic nonconvergence of Bayesian actions and beliefs. Urbana, Ill: College of Commerce and Business Administration, Bureau of Economic and Business Research, University of Illinois at Urbana-Champaign, 1990.

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8

Feldman, Mark. On the generic nonconvergence of Bayesian actions and beliefs. [Urbana, Ill]: College of Commerce and Business Administration, University of Illinois Urbana-Champaign, 1989.

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9

A, Gammerman, and UNICOM Seminars, eds. Probabilistic reasoning and Bayesian belief networks. Henley-on-Thames: Alfred Waller in association with UNICOM, 1995.

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10

Ramoni, Marco, and Paolo Sebastiani. Theory and Practice of Bayesian Belief Networks. A Hodder Arnold Publication, 2001.

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11

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

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12

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

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13

Sprenger, Jan, and Stephan Hartmann. Bayesian Philosophy of Science. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780199672110.001.0001.

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“Bayesian Philosophy of Science” addresses classical topics in philosophy of science, using a single key concept—degrees of beliefs—in order to explain and to elucidate manifold aspects of scientific reasoning. The basic idea is that the value of convincing evidence, good explanations, intertheoretic reduction, and so on, can all be captured by the effect it has on our degrees of belief. This idea is elaborated as a cycle of variations about the theme of representing rational degrees of belief by means of subjective probabilities, and changing them by a particular rule (Bayesian Conditionalization). Partly, the book is committed to the Carnapian tradition of explicating essential concepts in scientific reasoning using Bayesian models (e.g., degree of confirmation, causal strength, explanatory power). Partly, it develops new solutions to old problems such as learning conditional evidence and updating on old evidence, and it models important argument schemes in science such as the No Alternatives Argument, the No Miracles Argument or Inference to the Best Explanation. Finally, it is explained how Bayesian inference in scientific applications—above all, statistics—can be squared with the demands of practitioners and how a subjective school of inference can make claims to scientific objectivity. The book integrates conceptual analysis, formal models, simulations, case studies and empirical findings in an attempt to lead the way for 21th century philosophy of science.
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14

Applying Bayesian Belief Networks in Sun Tzu's Art of War. Storming Media, 2004.

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15

Probabilistic Reasoning and Bayesian Belief Networks (UNICOM - Information & Communications Technology). Nelson Thornes Ltd, 1998.

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16

Strategic Economic Decisionmaking Using Bayesian Belief Networks To Solve Complex Problems. Springer-Verlag New York Inc., 2012.

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17

Grover, Jeff. Strategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems. Springer, 2012.

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18

(Editor), Alexander Gammerman, J. G. Taylor (Editor), and V. Rayward-Smith (Editor), eds. Probabilistic Reasoning and Bayesian Belief Networks / Neural Networks / Applications of Modern Heuristic Methods. Alfred Waller, 1994.

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19

Donovan, Therese, and Ruth M. Mickey. Bayesian Statistics for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.001.0001.

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Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journey, from introductory probability to Bayesian inference and applications, including Markov Chain Monte Carlo approaches for parameter estimation, Bayesian belief networks, and decision trees. Detailed examples in each chapter contribute a great deal, where Bayes’ Theorem is at the front and center with transparent, step-by-step calculations. A vast amount of material is covered in a lighthearted manner; the journey is relatively pain-free. The book is intended to jump-start a reader’s understanding of probability, inference, and statistical vocabulary that will set the stage for continued learning. Other features include multiple links to web-based material, an annotated bibliography, and detailed, step-by-step appendices.
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20

Grover, Jeff. The Manual of Strategic Economic Decision Making: Using Bayesian Belief Networks to Solve Complex Problems. Springer, 2017.

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21

Grover, Jeff. The Manual of Strategic Economic Decision Making: Using Bayesian Belief Networks to Solve Complex Problems. Springer, 2018.

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22

Tang, Zhong. Developing complete conditional probability tables from fractional data for Bayesian belief networks in engineering decision making. 2005.

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23

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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24

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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25

Anjum, Rani Lill, and Stephen Mumford. Uncertainty, Certainty, and Beyond. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0019.

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The issue of probability enters into science because there can be inconclusive evidence, degrees of belief, and chancy phenomena in the world. This is relevant to Bayesian thinking, for example, which accepts that theories should be accepted only tentatively and considered more or less probable in the light of new evidence. Probability can be modelled in a simplified way, such as where a maximal degree of belief is assigned the value 1. A question remains of how well this reflects the reality of epistemic phenomena, which seems to allow cases where there is more than certainty, i.e. where you would still be certain of something even with less evidence than there is.
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26

Newen, Albert, Leon De Bruin, and Shaun Gallagher, eds. The Oxford Handbook of 4E Cognition. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198735410.001.0001.

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The Oxford Handbook of 4E Cognition provides a systematic overview of the state of the art in the field of 4E cognition: it includes chapters on hotly debated topics, for example, on the nature of cognition and the relation between cognition, perception and action; it discusses recent trends such as Bayesian inference and predictive coding; it presents new insights and findings regarding social understanding including the development of false belief understanding, and introduces new theoretical paradigms for understanding emotions and conceptualizing the interaction between cognition, language and culture. Each thematic section ends with a critical note to foster the fruitful discussion. In addition the final section of the book is dedicated to applications of 4E cognition approaches in disciplines such as psychiatry and robotics. This is a book with high relevance for philosophers, psychologists, psychiatrists, neuroscientists and anyone with an interest in the study of cognition as well as a wider audience with an interest in 4E cognition approaches.
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27

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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28

Schellenberg, Susanna. Justification, Luminosity, and Credences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198827702.003.0009.

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Chapter 8 discusses the repercussions of capacitism for the justification of beliefs, the credences we should assign to perceptual beliefs, and the luminosity of mental states. In light of this discussion, the chapter explores the consequences of capacitism for various familiar problem cases: speckled hens, identical twins, brains in vats, new evil demon scenarios, matrixes, and Swampman. I show why perceptual capacities are essential and cannot simply be replaced with representational content. I argue that the asymmetry between the employment of perceptual capacities in perception and their employment in relevant hallucinations and illusions is sufficient to account for the epistemic force of perceptual states yielded by employing such capacities. I show, moreover, why capacitism is compatible with standard Bayesian principles and how it accounts for degrees of justification. Finally, I discuss the relationship between evidence and rational confidence in light of an externalist view of perceptual content.
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29

Okasha, Samir. The Evolution–Rationality Connection. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815082.003.0007.

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There are two related dimensions to the evolution–rationality connection. The first is the evolution of rationality itself, thought of as an actual phenotypic attribute of some organisms; the second is the use of rationality-inspired concepts to describe evolved organisms, as in agential thinking. Rationality may be understood either as consistency of choice or as having good reasons for beliefs/actions; these notions have distinct domains of application. The adaptive significance of rationality over arationality is clear; what is less clear is whether evolution would always favour rationality over irrationality. In a simple model, an evolutionary basis for the norms of Bayesian rationality emerges; however, the model relies on restrictive assumptions. The possibility of an evolutionary naturalization of traditional rationality norms, though philosophically coherent, appears empirically unlikely.
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30

Encyclopedia of Computer Science and Technology: Volume 41 - Supplement 26 - Application of Bayesan Belief Networks to Highway Construction to Virtual ... of Computer Science and Technology). CRC, 1999.

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