About this topic
Summary Bayesian Reasoning includes issues related to: 1. the probabilistic logic of evidential support for hypotheses;  2. the logic of comparative belief, belief strengths, and belief updating as represented by classical probability functions; 3. the logic of decision as represented in terms of utilities, probabilities, and expected utility maximization, including ways in which this logic may represent comparative preferences among acts or states of affairs; 4. Bayesian probabilistic treatments of causal influence (e.g. via Bayes nets); 5. studies of relationships between human performance and models of reasoning and decision of a Bayesian kind (as described in 1-4 above).
Key works

Bayesian reasoning includes a wide variety of topics and issues. For introductory overviews of Bayesian confirmation theory and decision theory, among the best texts available are Skyrms 1966 and Hacking 2001; at a somewhat more advanced level Urbach & Howson 1993 is essential reading. Key sources for Bayesian probability and decision theory include Ramsey 2010Savage 1954Jeffrey 1965, and Joyce 1999. The classic treatment of Bayes nets is Pearl 1988Chater & Oaksford 2008 is an excellent collection of articles on Bayesian modeling of natural human reasoning. Also see the Stanford Encyclopedia of Philosophy (online, Zalta 2004) for helpful articles on various aspects of Bayesian reasoning: e.g. on Bayes' Theorem, Bayesian Epistemology, Inductive Logic, Decision Theory, etc.

Introductions Hájek 2008; Joyce 2008; Hawthorne 2011; Talbott 2008; Vineberg 2011; Weirich 2009; Hitchcock 2008.
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  1. Rational Decisions.Ken Binmore - 2008 - Princeton University Press.
    It is widely held that Bayesian decision theory is the final word on how a rational person should make decisions. However, Leonard Savage--the inventor of Bayesian decision theory--argued that it would be ridiculous to use his theory outside the kind of small world in which it is always possible to "look before you leap." If taken seriously, this view makes Bayesian decision theory inappropriate for the large worlds of scientific discovery and macroeconomic enterprise. When is it correct to use Bayesian (...)
  2. Science and ReligionCharles E. Raven.Marie Boas - 1954 - Isis 45 (2):205-206.
  3. Too Odd (Not) to Be True? A Reply to Olsson.Luc Bovens, Branden Fitelson, Stephan Hartmann & Josh Snyder - 2002 - British Journal for the Philosophy of Science 53 (4):539-563.
    Corroborating Testimony, Probability and Surprise’, Erik J. Olsson ascribes to L. Jonathan Cohen the claims that if two witnesses provide us with the same information, then the less probable the information is, the more confident we may be that the information is true (C), and the stronger the information is corroborated (C*). We question whether Cohen intends anything like claims (C) and (C*). Furthermore, he discusses the concurrence of witness reports within a context of independent witnesses, whereas the witnesses in (...)
  4. On the Equivalence of Goodman’s and Hempel’s Paradoxes.Kenneth Boyce - 2014 - Studies in History and Philosophy of Science Part A 45 (1):32-42.
    Historically, Nelson Goodman’s paradox involving the predicates ‘grue’ and ‘bleen’ has been taken to furnish a serious blow to Carl Hempel’s theory of confirmation in particular and to purely formal theories of confirmation in general. In this paper, I argue that Goodman’s paradox is no more serious of a threat to Hempel’s theory of confirmation than is Hempel’s own paradox of the ravens. I proceed by developing a suggestion from R. D. Rosenkrantz into an argument for the conclusion that these (...)
  5. Stopping at Language's Edge.J. P. Boyle - 1966 - World Futures 4 (4):93-95.
  6. JEFFREYS, H. -Theory of Probability. [REVIEW]R. B. Braithwaite - 1941 - Mind 50:198.
  7. Stopping Places.Marvin Bram - 2002 - Semiotica 141 (1/4):73-97.
  8. The Relation Between Induction and Probability--(Part II.).C. D. Broad - 1920 - Mind 29 (113):11-45.
  9. On the Relation Between Induction and Probability (Part I.).C. D. Broad - 1918 - Mind 27 (108):389-404.
  10. Bayesian Confirmation: A Means with No End.Peter Brössel & Franz Huber - 2015 - British Journal for the Philosophy of Science 66 (4):737-749.
    Any theory of confirmation must answer the following question: what is the purpose of its conception of confirmation for scientific inquiry? In this article, we argue that no Bayesian conception of confirmation can be used for its primary intended purpose, which we take to be making a claim about how worthy of belief various hypotheses are. Then we consider a different use to which Bayesian confirmation might be put, namely, determining the epistemic value of experimental outcomes, and thus to decide (...)
  11. The Concept of Probability. A Critical Survey of Recent Contributions.Charles E. Bures - 1938 - Philosophy of Science 5 (1):1-20.
  12. Theory of Logical Nets.Arthur W. Burks & Jesse B. Wright - 1954 - Journal of Symbolic Logic 19 (2):141-142.
  13. Charles Earle Raven: D.D., Hon. D.SC., F.B.A.F. H. C. Butler - 1965 - British Journal for the History of Science 2 (3):254-256.
  14. Evidentiary Fallacies and Empirical Data.Michael Byron - 2012 - American Philosophical Quarterly 49 (2):175.
    The Prosecutor's Fallacy is a well-known hazard in the assessment of probabilistic evidence that can lead to faulty inferences. It is perhaps best known via its role in the assessment of DNA match evidence in courts of law. A prosecutor, call him Burger, presents DNA evidence in court that links a defendant, Crumb, to a crime. The conditional probability of a DNA match given that Crumb is not guilty, or p(M | ~G), is very low: according to Burger, one chance (...)
  15. Philosophical Problems of Statistical Inference.B. C. - 1982 - Review of Metaphysics 35 (4):907-909.
  16. The Resistible Rise of Bayesian Thinking in Management: Historical Lessons From Decision Analysis.Laure Cabantous & Jean-Pascal Gond - 2015 - Journal of Management 41 (2):441-470.
    This paper draws from a case study of Decision Analysis – a discipline rooted in Bayesianism aimed at supporting managerial decision making – to inform the current discussion on the adoption of Bayesian modes of thinking in management research and practice. Relying on concepts from the Science, Technology and Society field of study, and Actor-Network Theory, we approach the production of scientific knowledge as a cultural, practical and material affair. Specifically, we analyze the activities deployed by decision analysts to overcome (...)
  17. Calibration and Probabilism.Michael Caie - 2014 - Ergo, an Open Access Journal of Philosophy 1.
  18. Component and Configurational Learning in Children: Additional Data.Joseph C. Campione, Michael McGrath & F. Michael Rabinowitz - 1971 - Journal of Experimental Psychology 88 (1):137.
  19. The Raven Paradox Revisited in Terms of Random Variables.Bruno Carbonaro & Federica Vitale - 2013 - Erkenntnis 78 (4):763-795.
    The discussion about the Raven Paradox is ever-renewing: after nearly 70 years, many authors propose from time to time new solutions, and many authors state that these solutions are unsatisfactory. It is worthy to be carefully noted that though most arguments in favor or against the paradox are based on the notion of “probability” and on the application of Bayes’ law, not one of them makes use of the Kolmogorov axiomatic theory of probability and on the subsequent notion of “random (...)
  20. Sampling, Probability and Causal Inference.Gösta Carlsson - 1952 - Theoria 18 (3):139-154.
  21. Notes on Probability and Induction.Rudolf Carnap - 1973 - Synthese 25 (3-4):269 - 298.
  22. Probabilistic Models of Cognition: Where Next.N. Carter, J. B. Tenenbaum & A. Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
  23. Against Modularity, the Causal Markov Condition, and Any Link Between the Two: Comments on Hausman and Woodward.Nancy Cartwright - 2002 - British Journal for the Philosophy of Science 53 (3):411-453.
    In their rich and intricate paper ‘Independence, Invariance, and the Causal Markov Condition’, Daniel Hausman and James Woodward ([1999]) put forward two independent theses, which they label ‘level invariance’ and ‘manipulability’, and they claim that, given a specific set of assumptions, manipulability implies the causal Markov condition. These claims are interesting and important, and this paper is devoted to commenting on them. With respect to level invariance, I argue that Hausman and Woodward's discussion is confusing because, as I point out, (...)
  24. Models for Prediction, Explanation and Control.Lorenzo Casini, Phyllis Mckay Illari, Federica Russo & Jon Williamson - 2011 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (1):5-33.
    The Recursive Bayesian Net formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how (...)
  25. What Do We Really Measure and What Relevance has the Data to Us Personally? Are Measurements and Their Interpretations Biased by Our Subjective Views?Alexander Cetkovic - 2012 - Technoetic Arts 9 (2):301-306.
  26. Many Reasons or Just One: How Response Mode Affects Reasoning in the Conjunction Problem.Ralph Hertwig Valerie M. Chase - 1998 - Thinking and Reasoning 4 (4):319 – 352.
    Forty years of experimentation on class inclusion and its probabilistic relatives have led to inconsistent results and conclusions about human reasoning. Recent research on the conjunction "fallacy" recapitulates this history. In contrast to previous results, we found that a majority of participants adhere to class inclusion in the classic Linda problem. We outline a theoretical framework that attributes the contradictory results to differences in statistical sophistication and to differences in response mode-whether participants are asked for probability estimates or ranks-and propose (...)
  27. Issues in Statistical Inference.Dr Siu L. Chow - 2002 - Philosophical Explorations.
    The APA Task Force’s treatment of research methods is critically examined. The present defense of the experiment rests on showing that (a) the control group cannot be replaced by the contrast group, (b) experimental psychologists have valid reasons to use non-randomly selected subjects, (c) there is no evidential support for the experimenter expectancy effect, (d) the Task Force had misrepresented the role of inductive and deductive logic, and (e) the validity of experimental data does not require appealing to the effect (...)
  28. Statistics and its Role in Psychological Research.Siu L. Chow - manuscript
    How one may use descriptive statistics to give a succinct description of research data is first discussed. The probability basis of inferential statistics, namely, the random sampling distribution of the test statistic, is then introduced. The said sampling distribution is used to introduced the null-hypothesis significance-testing procedure (NHSTP). The emphasis on 'procedure' serves to highlight the fact that significance tests are about data, not about the substantive hypothesis. The distinction is made between (a) the statistical alternative hypothesis (H1) and the (...)
  29. Dutch-Book Arguments Depragmatized: Epistemic Consistency For Partial Believers.David Christensen - 1996 - Journal of Philosophy 93 (9):450-479.
  30. Bioinformatics.Tianjiao Chu - unknown
    Motivation: One approach to inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization is to estimate the associations of gene expression levels measured in repeated samples. The associations may be estimated by correlation coefficients or by conditional frequencies or by some other statistic. Although these procedures have been successfully applied to other areas, their validity when applied to microarray measurements has yet to be tested. Results: This paper describes an elementary statistical difficulty for all such procedures, no matter (...)
  31. Book Review:Techniques of Statistical Analysis Statistical Research Group; Sampling Inspection Statistical Research Group. [REVIEW]C. West Churchman - 1949 - Philosophy of Science 16 (1):88-.
  32. Introduction: Thinking Possibilistically in a Probabilistic World.Lee Clarke - 2008 - Social Research: An International Quarterly 75 (3):931-936.
  33. What has Probability to Do with Strength of Belief.L. Jonathan Cohen - 1993 - In J. Dubucs (ed.), Philosophy of Probability. Kluwer, Dordrecht. pp. 129--143.
  34. On Three Measures of Explanatory Power with Axiomatic Representations.Michael P. Cohen - 2016 - British Journal for the Philosophy of Science 67 (4):1077-1089.
    Jonah N. Schupbach and Jan Sprenger and Vincenzo Crupi and Katya Tentori have recently proposed measures of explanatory power and have shown that they are characterized by certain arguably desirable conditions or axioms. I further examine the properties of these two measures, and a third measure considered by I. J. Good and Timothy McGrew . This third measure also has an axiomatic representation. I consider a simple coin-tossing example in which only the Crupi–Tentori measure does not perform well. The Schupbach–Sprenger (...)
  35. On Schupbach and Sprenger’s Measures of Explanatory Power.Michael P. Cohen - 2015 - Philosophy of Science 82 (1):97-109.
    Jonah N. Schupbach and Jan Sprenger have proposed conditions of adequacy for measures of explanatory power. They derive and defend a measure of explanatory power satisfying their conditions of adequacy. This article furthers the development of their measure. The requirement that the measure be multidimensional analytic is avoided. Several proofs are simplified, and gaps in proofs are filled.
  36. Lotteries and the Close Shave Principle.John Collins - 2006 - In Stephen Hetherington (ed.), Aspects of Knowing. Elsevier Science. pp. 83.
  37. Synchronicity: Science, Myth, and the Trickster.Allan Combs - 1996 - Marlowe & Co..
  38. A Paradox in Hempel's Criterion of Maximal Specificity.Roger M. Cooke - 1981 - Philosophy of Science 48 (2):327-328.
  39. An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality.Gregory F. Cooper, Constantin F. Aliferis, Richard Ambrosino, John Aronis, Bruce G. Buchanon, Richard Caruana, Michael J. Fine, Clark Glymour, Geoffrey Gordon, Barbara H. Hanusa, Janine E. Janosky, Christopher Meek, Tom Mitchell, Thomas Richardson & Peter Spirtes - unknown
    This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a model’s potential to assist (...)
  40. The Conjunction Fallacy.Jack Copeland & Diane Proudfoot - 2003 - Logique Et Analyse 46.
  41. Predictive Laws of Association in Statistics and Physics.D. Costantini & U. Garibaldi - 1996 - Erkenntnis 45 (2-3):399 - 422.
    In the present paper we face the problem of estimating cell probabilities in the case of a two-dimensional contingency table from a predictive point of view. The solution is given by a double stochastic process. The first subprocess, the unobservable one, is supposed to be exchangeable and invariant. For the second subprocess, the observable one, we suppose it is independent conditional on the first one.
  42. Probability Theory Plus Noise: Replies to Crupi and Tentori and to Nilsson, Juslin, and Winman.Fintan Costello & Paul Watts - 2016 - Psychological Review 123 (1):112-123.
  43. Objectivity and Conditionality in Frequentist Inference.David Cox & Deborah G. Mayo - 2010 - In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. pp. 276.
  44. An Argument for Not Equating Confirmation and Explanatory Power.Vincenzo Crupi - 1996 - Philosophy of Science 63:21-6.
  45. Noisy Probability Judgment, the Conjunction Fallacy, and Rationality: Comment on Costello and Watts.Vincenzo Crupi & Katya Tentori - 2016 - Psychological Review 123 (1):97-102.
  46. State of the Field: Measuring Information and Confirmation.Vincenzo Crupi & Katya Tentori - 2014 - Studies in History and Philosophy of Science Part A 47:81-90.
  47. Irrelevant Conjunction: Statement and Solution of a New Paradox.Vincenzo Crupi & Katya Tentori - 2010 - Philosophy of Science 77 (1):1-13.
    The so‐called problem of irrelevant conjunction has been seen as a serious challenge for theories of confirmation. It involves the consequences of conjoining irrelevant statements to a hypothesis that is confirmed by some piece of evidence. Following Hawthorne and Fitelson, we reconstruct the problem with reference to Bayesian confirmation theory. Then we extend it to the case of conjoining irrelevant statements to a hypothesis that is dis confirmed by some piece of evidence. As a consequence, we obtain and formally present (...)
  48. Tractable Inference for Probabilistic Data Models.Lehel Csato, Manfred Opper & Ole Winther - 2003 - Complexity 8 (4):64-68.
  49. Additional Confirmation for the Effect of Environmental Light Intensity on the Seasonality of Human Conceptions.David R. Cummings - 2007 - Journal of Biosocial Science 39 (3):383.
  50. Performance of Resampling Methods Based on Decision Trees, Parametric and Nonparametric Bayesian Classifiers for Three Medical Datasets.Małgorzata M. Ćwiklińska-Jurkowska - 2013 - Studies in Logic, Grammar and Rhetoric 35 (1).
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