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Books on the topic 'Bayesian rules'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Gubler, Philipp. A Bayesian Analysis of QCD Sum Rules. Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54318-3.

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2

Gubler, Philipp. A Bayesian Analysis of QCD Sum Rules. Springer Japan, 2013.

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3

Srivastava, M. S. Optimal bayes stopping rules for detecting the change point in a bernoulli process. University of Toronto, Dept. of Statistics, 1989.

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4

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

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5

Mat, Leo. The Bayesian rule: Simplification and geometrical visualization. Roderer, 1998.

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6

Duo, Qin. Has Bayesian estimation principle ever used Bayes' rule? London University, Queen Mary and Westfield College, Department of Economics, 1994.

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7

Federico, Sturzenegger, and Harvard University. Center for International Development, eds. Estimating SARB's policy reaction rule. Center for International Development at Harvard University, 2008.

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8

Bayesian Analysis Of Qcd Sum Rules. Springer Verlag, Japan, 2013.

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9

Gubler, Philipp. Bayesian Analysis of QCD Sum Rules. Springer Japan, 2015.

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10

Gubler, Philipp. A Bayesian Analysis of QCD Sum Rules. Springer, 2013.

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11

Titelbaum, Michael G. Fundamentals of Bayesian Epistemology 2. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192863140.001.0001.

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This book introduces readers to the fundamentals of Bayesian epistemology. It begins by motivating and explaining the idea of a degree of belief (also known as a “credence”). It then presents Bayesians’ five core normative rules governing degrees of belief: Kolmogorov’s three probability axioms, the Ratio Formula for conditional credences, and Conditionalization for updating credences over time. After considering a few proposed additions to these norms, it applies the core rules to confirmation and decision theory. The book then details arguments for the Bayesian rules based on representation

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12

Titelbaum, Michael G. Fundamentals of Bayesian Epistemology 1. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198707608.001.0001.

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This book introduces readers to the fundamentals of Bayesian epistemology. It begins by motivating and explaining the idea of a degree of belief (also known as a “credence”). It then presents Bayesians’ five core normative rules governing degrees of belief: Kolmogorov’s three probability axioms, the Ratio Formula for conditional credences, and Conditionalization for updating credences over time. After considering a few proposed additions to these norms, it applies the core rules to confirmation and decision theory. The book then details arguments for the Bayesian rules based on representation

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13

Johnson, Alicia A., Miles Q. Ott, and Mine Dogucu. Bayes Rules!: An Introduction to Applied Bayesian Modeling. Taylor & Francis Group, 2022.

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14

Johnson, Alicia A., Miles Q. Ott, and Mine Dogucu. Bayes Rules!: An Introduction to Applied Bayesian Modeling. Taylor & Francis Group, 2022.

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15

Johnson, Alicia A., Miles Q. Ott, and Mine Dogucu. Bayes Rules!: An Introduction to Applied Bayesian Modeling. Taylor & Francis Group, 2022.

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16

Marques, Ricardo, Christian Bouville, Luís Paulo Santos, and Kadi Bouatouch. Efficient Quadrature Rules for Illumination Integrals: From Quasi Monte Carlo to Bayesian Monte Carlo. Springer International Publishing AG, 2015.

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17

Marques, Ricardo, Christian Bouville, Luís Paulo Santos, and Kadi Bouatouch. Efficient Quadrature Rules : For Illumination Integrals: From Quasi Monte Carlo to Bayesian Monte Carlo. Morgan & Claypool Publishers, 2015.

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18

Marques, Ricardo, Christian Bouville, Luís Paulo Santos, and Kadi Bouatouch. Efficient Quadrature Rules for Illumination Integrals: From Quasi Monte Carlo to Bayesian Monte Carlo. Morgan & Claypool Publishers, 2015.

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19

Sequential Change Detection and Hypothesis Testing: General Non-I. I. D. Stochastic Models and Asymptotically Optimal Rules. Taylor & Francis Group, 2019.

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20

Tartakovsky, Alexander. Sequential Change Detection and Hypothesis Testing: General Non-I. I. D. Stochastic Models and Asymptotically Optimal Rules. Taylor & Francis Group, 2019.

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21

Tartakovsky, Alexander. Sequential Change Detection and Hypothesis Testing: General Non-I. I. D. Stochastic Models and Asymptotically Optimal Rules. Taylor & Francis Group, 2019.

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22

Sprenger, Jan. Confirmation and Induction. Edited by Paul Humphreys. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199368815.013.10.

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Scientific knowledge is based on induction, ampliative inferences from experience. The chapter gives an overview of the problem of induction and the responses that philosophers of science have developed over time, focusing on attempts to spell out rules of inductive inference, and to balance attractive theoretical principles with judgments and intuitions in particular cases. That this is not always easy is demonstrated by challenges such as the paradox of the ravens, the problem of irrelevant conjunctions, and Goodman's new riddle of induction. The chapter then focuses on explications of the d

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23

Heiner, Karl W., Marc Kennedy, and Anthony O'Hagan. Sequential multilocation auditing and the New York food stamps program. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.23.

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This article discusses the use of Bayesian methods in analysing data that evolve over time in sequential multilocation auditing. Using the New York food stamps program as a case study, it proposes a model that incorporates a nonparametric component for the error magnitudes (taints), a hierarchical model for overall error rates across counties and parameters controlling the variation of rates from one year to the next, including an overall trend in error rates. The article first provides an overview of the New York food stamps program, along with the auditing concepts and terminology, before in

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24

Bayes' Rule. Sebtel Press, 2013.

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25

McGrew, Timothy. The Spirit of Cromwell’s Rule. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198746904.003.0015.

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One of the central complaints about Bayesian probability is that it places no constraints on individual subjectivity in one’s initial probability assignments. Those sympathetic to Bayesian methods have responded by adding restrictions motivated by broader epistemic concerns about the possibility of changing one’s mind. This chapter explores some cases where, intuitively, a straightforward Bayesian model yields unreasonable results. Problems arise in these cases not because there is something wrong with the Bayesian formalism per se but because standard textbook illustrations teach us to repres

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26

Stone, Dr James V. Bayes' Rule: A Tutorial Introduction to Bayesian Analysis. Sebtel Press, 2013.

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27

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 Conditionaliza

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28

Westheimer, Gerald. The Shifted-Chessboard Pattern as Paradigm of the Exegesis of Geometrical-Optical Illusions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0036.

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The shifted chessboard or café wall illusion yields to analysis at the two poles of the practice of vision science: bottom-up, pursuing its course from the visual stimulus into the front end of the visual apparatus, and top-down, figuring how the rules governing perception might lead to it. Following the first approach, examination of the effects of light spread in the eye and of nonlinearity and center-surround antagonism in the retina has made some inroads and provided partial explanations; with respect to the second, principles of perspective and of continuity and smoothness of contours can

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29

Bayes' Rule with Python: A Tutorial Introduction to Bayesian Analysis. Sebtel Press, 2016.

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30

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 peo

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31

Pennello, Gene A. Multiple comparisons for the balanced two-way factorial: An applied Bayes rule (k-ratio) approach. 1993.

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32

Hoang, Lê Nguyên. Equation of Knowledge: From Bayes' Rule to a Unified Philosophy of Science. Taylor & Francis Group, 2020.

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33

Hoang, Lê Nguyên. Equation of Knowledge: From Bayes' Rule to a Unified Philosophy of Science. Taylor & Francis Group, 2020.

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34

Equation of Knowledge: From Bayes' Rule to a Unified Philosophy of Science. Taylor & Francis Group, 2020.

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35

McGrayne, Sharon Bertsch. Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy. Yale University Press, 2011.

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36

McGrayne, Sharon Bertsch. Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy. Yale University Press, 2011.

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37

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy. Yale University Press, 2012.

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38

The Theory That Would Not Die: How Bayes' rule cracked the Enigma code, hunted down Russian submarines, and emerged triumphant from two centuries of controversy. Yale University Press, 2011.

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