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 Howson & Urbach 1993 is essential reading. Key sources for Bayesian probability and decision theory include Ramsey 2010Savage 1954Jeffrey 1983, 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. Mark Alfano (2007). A Critical Discussion Of The Compatibility Of Bayesianism And Inference To The Best Explanation. Philosophical Writings 34 (1).
    In this paper I critique Peter Lipton’s attempt to deal with the threat of Bayesianism to the normative aspect of his project in Inference to the Best Explanation. I consider the five approaches Lipton proposes for reconciling the doxastic recommendations of Inference to the Best Explanation with BA’s: IBE gives a ‘boost’ to the posterior probability of particularly ‘lovely’ hypotheses after the Bayesian calculation is performed; IBE helps us to set the likelihood of evidence on a given hypothesis; IBE helps (...)
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  2. F. C. Benenson (1976). Probability, Frequency and Evidence.
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  3. S. Blackburn (1975). SWINBURNE, R. "An Introduction to Confirmation Theory". [REVIEW] Mind 84:146.
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  4. A. F. Chalmers (1981). Howson, Colin , "Method and Appraisal in the Physical Sciences". [REVIEW] Erkenntnis 16:167.
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  5. Kelly James Clark (1985). Probabilistic Confirmation Theory and the Existence of God. Dissertation, University of Notre Dame
    A recent development in the philosophy of religion has been the attempt to justify belief in God using Bayesian confirmation theory. My dissertation critically discusses two prominent spokesmen for this approach--Richard Swinburne and J. L. Mackie. Using probabilistic confirmation theory, these philosophers come to wildly divergent conclusions with respect to the hypothesis of theism; Swinburne contends that the evidence raises the overall probability of the hypothesis of theism, whereas Mackie argues that the evidence disconfirms the existence of God. After a (...)
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  6. David Cox & Deborah G. Mayo (2010). Objectivity and Conditionality in Frequentist Inference. 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. 276.
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  7. Yehudah Freundlich (1976). Resurrecting the Ravens. Synthese 33 (1):341 - 354.
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  8. Lisbeth S. Fried & Cameron R. Peterson (1969). Information Seeking: Optional Versus Fixed Stopping. Journal of Experimental Psychology 80 (3p1):525.
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  9. Michael Friedman (1979). Truth and Confirmation. Journal of Philosophy 76 (7):361-382.
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  10. Zoubin Ghahramani (2002). Graphical Models: Parameter Learning. In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. Mit Press. 2--486.
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  11. Jerzy Giedymin (1968). Confirmation, Counterfactuals and Projectibility. In Raymond Klibansky (ed.), Contemporary Philosophy. Firenze, la Nuova Italia. 2--70.
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  12. George F. Gilder & John Wohlstetter (forthcoming). Broadband or Bust! Inquiry.
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  13. Daniel Gile (1999). Testing the Effort Models' Tightrope Hypothesis in Simultaneous Interpreting-A Contribution. Hermes 23 (1999):153-172.
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  14. D. A. Gillies (1972). The Subjective Theory of Probability. [REVIEW] British Journal for the Philosophy of Science 23 (2):138-157.
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  15. Donald Gillies (2001). Bayesianism and the Fixity of the Theoretical Framework. In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. 363--379.
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  16. Donald Gillies (2001). Critical Notices-Judea Pearl Causality: Models, Reasoning, and Inference. British Journal for the Philosophy of Science 52 (3):613-622.
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  17. Donald Gillies (1990). Bayesianism Versus Falsifigationism. Ratio 3 (1):82-98.
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  18. T. R. Girill (1978). Are Requirement and Confirmation Analogous? Philosophical Studies 33 (4):339 - 349.
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  19. R. Goldstone (1992). The Effects of Feature Distribution on Estimation. Bulletin of the Psychonomic Society 30 (6):480-481.
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  20. Rebecca Gómez (2009). Statistical Learning in Infant Language Development. In Gareth Gaskell (ed.), Oxford Handbook of Psycholinguistics. Oup Oxford.
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  21. Christophe Gonzales & Pierre-Henri Wuillemin (2011). PRM Inference Using Jaffray & Faÿ's Local Conditioning. Theory and Decision 71 (1):33-62.
    Probabilistic Relational Models (PRMs) are a framework for compactly representing uncertainties (actually probabilities). They result from the combination of Bayesian Networks (BNs), Object-Oriented languages, and relational models. They are specifically designed for their efficient construction, maintenance and exploitation for very large scale problems, where BNs are known to perform poorly. Actually, in large-scale problems, it is often the case that BNs result from the combination of patterns (small BN fragments) repeated many times. PRMs exploit this feature by defining these patterns (...)
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  22. I. J. Good, Ian Hacking, R. C. Jeffrey & Håkan Törnebohm (1966). The Estimation of Probabilities: An Essay on Modern Bayesian Methods. Synthese 16 (2):234-244.
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  23. Mark Goodale (2011). Becoming Irrelevant. In Thomas Cushman (ed.), Handbook of Human Rights. Routledge. 180.
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  24. Nelson Goodman (1947). On Infirmities of Confirmation-Theory. Philosophy and Phenomenological Research 8 (1):149-151.
  25. Noah D. Goodman, Chris L. Baker & Joshua B. Tenenbaum (2009). Cause and Intent: Social Reasoning in Causal Learning. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 2759--2764.
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  26. Jacqueline Jarrett Goodnow & Leo Postman (1955). Probability Learning in a Problem-Solving Situation. Journal of Experimental Psychology 49 (1):16.
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  27. Alison Gopnik, Causal Learning Across Domains.
    Five studies investigated (a) children’s ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of biology and psychology. Experiment 2 replicated the results in the domain of biology with a more complex pattern of conditional dependencies. In Experiment 3, children used evidence about patterns of dependence and independence (...)
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  28. Gerd Graßhoff (2011). Inferences to Causal Relevance From Experiments. In Dennis Dieks, Wenceslao Gonzalo, Thomas Uebel, Stephan Hartmann & Marcel Weber (eds.), Explanation, Prediction, and Confirmation. Springer. 167--182.
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  29. Christopher D. Green (2002). Comment on Chow's "Issues in Statistical Inference". Philosophical Explorations.
    Contrary to Chow, Wilkinson's report, though more tentative than it might have been, is a reasoned and valuable contribution to psychological science. For those who are quite familiar with the details of statistical methods, it confirms much of what has been happening in the literature over the past few decades. For those who have not been keeping abreast of new developments on the statistical scene, it alerts them in a gentle way that there have been some important changes since they (...)
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  30. Nicholas Griffin (1975). Has Harre Solved Hempel's Paradox? Mind 84 (335):426-430.
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  31. Barbro Gustafsson & Ingmar Pörn (1994). A Motivational Approach to Confirmation: An Interpretation of Dysphagic Patients' Experiences. Theoretical Medicine and Bioethics 15 (4).
    In this paper we articulate confirmation and disconfirmation as components in human motivation. We develop a theory of motivation on the basis of a model of human action and we explore aspects of confirmation and disconfirmation in the context of the meeting of dysphagic patients with their physicians. We distinguish four central elements in confirmation and disconfirmation and use these and the relations between them for the purpose of constructing a typology. Finally, on the basis of the results obtained we (...)
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  32. Alan H.Ájek (1998). Agnosticism Meets Bayesianism. Analysis 58 (3):199-206.
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  33. Ian Hacking (1968). On Falling Short of Strict Coherence. Philosophy of Science 35 (3):284-286.
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  34. Alan Hajek (2008). Probability—A Phifosophical Overview. In Bonnie Gold & Roger Simons (eds.), Proof and Other Dilemmas: Mathematics and Philosophy. Mathematical Association of America. 323.
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  35. Alan Hájek & James M. Joyce (2008). Confirmation. In S. Psillos & M. Curd (eds.), The Routledge Companion to the Philosophy of Science. Routledge.
    Confirmation theory is intended to codify the evidential bearing of observations on hypotheses, characterizing relations of inductive “support” and “counter­support” in full generality. The central task is to understand what it means to say that datum E confirms or supports a hypothesis H when E does not logically entail H.
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  36. Marsha Hanen (1975). Confirmation, Explanation and Acceptance. In Keith Lehrer (ed.), Analysis and Metaphysics. Springer. 93--128.
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  37. Robin Hanson (2006). Uncommon Priors Require Origin Disputes. Theory and Decision 61 (4):319-328.
    In standard belief models, priors are always common knowledge. This prevents such models from representing agents’ probabilistic beliefs about the origins of their priors. By embedding standard models in a larger standard model, however, pre-priors can describe such beliefs. When an agent’s prior and pre-prior are mutually consistent, he must believe that his prior would only have been different in situations where relevant event chances were different, but that variations in other agents’ priors are otherwise completely unrelated to which events (...)
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  38. Maralee Harrell & Clark Glymour (2002). Confirmation And Chaos. Philosophy of Science 69 (2):256-265.
    Recently, Rueger and Sharp (1996) and Koperski (1998) have been concerned to show that certain procedural accounts of model confirmation are compromised by non-linear dynamics. We suggest that the issues raised are better approached by considering whether chaotic data analysis methods allow for reliable inference from data. We provide a framework and an example of this approach.
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  39. Maralee Harrell & Clark Glymour (2002). Confirmation and Chaos. Philosophy of Science 69 (2):256-265.
    Recently, Rueger and Sharp (1996) and Koperski (1998) have been concerned to show that certain procedural accounts of model confirmation are compromised by non‐linear dynamics. We suggest that the issues raised are better approached by considering whether chaotic data analysis methods allow for reliable inference from data. We provide a framework and an example of this approach.
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  40. Evan Heit (2008). Models of Inductive Reasoning. In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. 322--338.
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  41. Casey Helgeson, The Confirmational Significance of Agreeing Measurements.
    Agreement between \independent" measurements of a theoretically posited quantity is intuitively compelling evidence that a theory is, loosely speaking, on the right track. But exactly what conclusion is warranted by such agreement? I propose a new account of the phenomenon's epistemic significance within the framework of Bayesian epistemology. I contrast my proposal with the standard Bayesian treatment, which lumps the phenomenon under the heading of \evidential diversity.".
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  42. Carl G. Hempel (1945). Studies in the Logic of Confirmation (I.). Mind 54 (213):1-26.
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  43. Carl G. Hempel (1945). Studies in the Logic of Confirmation (II.). Mind 54 (214):97-121.
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  44. Carl G. Hempel & Paul Oppenheim (1945). A Definition of "Degree of Confirmation". Philosophy of Science 12 (2):98-115.
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  45. Vincent F. Hendricks, Stig Andur Pederson & Klaus Frovin Jørgensen (eds.) (2001). Probability Theory: Philosophy, Recent History and Relations to Science. Synthese Library, Kluwer.
    This book sheds light on some recent discussions of the problems in probability theory and their history, analysing their philosophical and mathematical significance, and the role pf mathematical probability theory in other sciences.
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  46. Christian Hennig (2012). Deborah G. Mayo & Aris Spanos, Eds. 2009. Error and Inference (Christian Hennig). Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 27 (2):245-247.
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  47. Jaakko Hintikka (1970). Unknown Probabilities, Bayesianism, and de Finetti's Representation Theorem. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1970:325 - 341.
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  48. R. J. Hirst & S. F. Barker (1960). Induction and Hypothesis: A Study of the Logic of Confirmation. Philosophical Quarterly 10 (41):375.
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  49. Gábor Hofer-Szabó (forthcoming). Relating Bell’s Local Causality to the Causal Markov Condition. Foundations of Physics:1-27.
    The aim of the paper is to relate Bell’s notion of local causality to the Causal Markov Condition. To this end, first a framework, called local physical theory, will be introduced integrating spatiotemporal and probabilistic entities and the notions of local causality and Markovity will be defined. Then, illustrated in a simple stochastic model, it will be shown how a discrete local physical theory transforms into a Bayesian network and how the Causal Markov Condition arises as a special case of (...)
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  50. Ulrich Hoffrage & Gerd Gigerenzer (1996). The Impact of Information Representation on Bayesian Reasoning. In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. 126--130.
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