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 1926Savage 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. 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.
  2. Factors Determining the Probability of Recollection of Intraoperative Events.L. Goldman - 1990 - In B. Bonke, W. Fitch & K. Millar (eds.), Memory and Awareness in Anesthesia. Swets & Zeitlinger. pp. 45--9.
  3. Differential Nets, Experiments and Reduction.Giulio Guerrieri - unknown
  4. Updating and Improvement of the Highresolution (1km X 1km, 1h) Emission Model for Spain.M. Guevara, G. Arévalo, S. Gassó, F. Martínez, A. Soret, G. Ferrer & J. M. Baldasano - 2012 - Hermes 2.
  5. Hierarchical Holographic Modeling for Conflict Resolution.Y. Y. Haimes & A. Weiner - 1986 - Philosophy of Science 53 (2):200-222.
  6. Subjective Probability and its Dynamics.Alan Hajek & Julia Staffel - forthcoming - In Markus Knauff & Wolfgang Spohn (eds.), MIT Handbook of Rationality. MIT Press.
    This chapter is a philosophical survey of some leading approaches in formal epistemology in the so-called ‘Bayesian’ tradition. According to them, a rational agent’s degrees of belief—credences—at a time are representable with probability functions. We also canvas various further putative ‘synchronic’ rationality norms on credences. We then consider ‘diachronic’ norms that are thought to constrain how credences should respond to evidence. We discuss some of the main lines of recent debate, and conclude with some prospects for future research.
  7. Swans, Ravens, Death and Tyranny: On the Mythology of Freedom.Wendy C. Hamblet - 2009 - Philosophical Frontiers: A Journal of Emerging Thought 4 (2).
  8. Starting and Stopping.C. L. Hamblin - 1969 - The Monist 53 (3):410-425.
  9. Why Do We Need to Employ Bayesian Statistics and How Can We Employ It in Studies of Moral Education?: With Practical Guidelines to Use JASP for Educators and Researchers.Hyemin Han - forthcoming - Journal of Moral Education:1-19.
    In this paper, we discuss the benefits of and how to utilize Bayesian statistics in studies of moral education. To demonstrate concrete examples of the applications of Bayesian statistics to studies of moral education, we reanalyzed two datasets previously collected: one small dataset collected from a moral educational intervention experiment, and one big dataset from a large-scale Defining Issues Test-2 survey. Results suggest that Bayesian analysis of datasets collected from moral educational studies can provide additional useful statistical information, particularly that (...)
  10. Learning Linear Causal Structure Equation Models with Genetic Algorithms.Shane Harwood & Richard Scheines - unknown
    Shane Harwood and Richard Scheines. Learning Linear Causal Structure Equation Models with Genetic Algorithms.
  11. Measures for Improving Support for the Gifted.Kurt A. Heller - unknown
  12. Measures Over Initial Conditions.Meir Hemmo & Orly Shenker - 2012 - In Yemima Ben-Menahem & Meir Hemmo (eds.), Probability in Physics. Springer. pp. 87--98.
    This paper concerns the meaning of the idea of typicality in classical statistical mechanics and how typicality is related to the notion of probability.
  13. Mayo & Spanos, Eds. 2009. Error and Inference.Christian Hennig - 2012 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 27 (2):245-247.
  14. Probability and the Logic of Rational Belief.J. Henry E. Kyburg - 1961 - Middletown, Ct: Wesleyan University Press.
  15. Bayesianism and Scientific Inference.Mary Hesse - 1975 - Studies in History and Philosophy of Science Part A 5 (4):367-370.
  16. Response Probability in a Two-Choice Learning Situation with Varying Probability of Reinforcement.Robert H. Hickson - 1961 - Journal of Experimental Psychology 62 (2):138.
  17. Subjective Probability: The Real Thing. [REVIEW]Matthias Hild - 2006 - Vienna Circle Institute Yearbook 12:228-230.
  18. The Probability of Induction.Donald J. Hillman - 1963 - Philosophical Studies 14 (4):51 - 56.
  19. The Interrogative Approach to Inquiry and Probabilistic Inference.Jaakko Hintikka - 1987 - Erkenntnis 26 (3):429 - 442.
  20. Minimax Point Estimation.J. L. Hodges & E. L. Lehman - 1950 - Annals of Mathematical Statistics 21:182--197.
  21. In Search of Good Probability Assessors: An Experimental Comparison of Elicitation Rules for Confidence Judgments.Guillaume Hollard, Sébastien Massoni & Jean-Christophe Vergnaud - 2016 - Theory and Decision 80 (3):363-387.
  22. General Methods: Statistical Analysis and Comparison.Karl J. Holzinger - 1938 - In Guy Montrose Whipple (ed.), The Scientific Movement in Education. Bloomington: Ill.. pp. 293--306.
  23. Introduction to Scientific Inference.Robert Hooke - 1963 - San Francisco, Holden-Day.
  24. An Evaluation of Subjective Probability in a Visual Discrimination Task.William C. Howell - 1967 - Journal of Experimental Psychology 75 (4):479.
  25. Interpreting Probability: Controversies and Developments in the Early Twentieth Century.David Howie - 2002 - Cambridge University Press.
    The term probability can be used in two main senses. In the frequency interpretation it is a limiting ratio in a sequence of repeatable events. In the Bayesian view, probability is a mental construct representing uncertainty. This 2002 book is about these two types of probability and investigates how, despite being adopted by scientists and statisticians in the eighteenth and nineteenth centuries, Bayesianism was discredited as a theory of scientific inference during the 1920s and 1930s. Through the examination of a (...)
  26. Repelling a Prussian Charge with a Solution to a Paradox of Dubins.Colin Howson - 2018 - Synthese 195 (1).
    Pruss uses an example of Lester Dubins to argue against the claim that appealing to hyperreal-valued probabilities saves probabilistic regularity from the objection that in continuum outcome-spaces and with standard probability functions all save countably many possibilities must be assigned probability 0. Dubins’s example seems to show that merely finitely additive standard probability functions allow reasoning to a foregone conclusion, and Pruss argues that hyperreal-valued probability functions are vulnerable to the same charge. However, Pruss’s argument relies on the rule of (...)
  27. Does Information Inform Confirmation?Colin Howson - 2016 - Synthese 193 (7):2307-2321.
    In a recent survey of the literature on the relation between information and confirmation, Crupi and Tentori claim that the former is a fruitful source of insight into the latter, with two well-known measures of confirmation being definable purely information-theoretically. I argue that of the two explicata of semantic information which are considered by the authors, the one generating a popular Bayesian confirmation measure is a defective measure of information, while the other, although an admissible measure of information, generates a (...)
  28. A Unified Probabilistic Inference Model for Targeted Marketing.Jiajin Huang & Ning Zhong - 2008 - In S. Iwata, Y. Oshawa, S. Tsumoto, N. Zhong, Y. Shi & L. Magnani (eds.), Communications and Discoveries From Multidisciplinary Data. Springer. pp. 171--186.
  29. Statistical Approach Involving Bayes' Theorem and the Estimation of the Prior Distribution.Hirosi Hudimoto - 1971 - Annals of the Japan Association for Philosophy of Science 4 (1):35-45.
  30. Bayesian Convergence to the Truth and the Metaphysics of Possible Worlds.Simon M. Huttegger - 2015 - Philosophy of Science 82 (4):587-601.
    In a recent paper, Belot argues that Bayesians are epistemologically flawed because they believe with probability 1 that they will learn the truth about observational propositions in the limit. While Belot’s considerations suggest that this result should be interpreted with some care, the concerns he raises can largely be defused by putting convergence to the truth in the context of learning from an arbitrarily large but finite number of observations.
  31. Exploring the Effect of a User’s Personality Traits on Tactile Communication with a Robot Using Bayesian Networks.Jungsik Hwang & Kun Chang Lee - 2015 - Interaction Studiesinteraction Studies Social Behaviour and Communication in Biological and Artificial Systems 16 (1):29-53.
  32. Field Failure Prediction Using Bayesian Network.Kei Imazawa & Yoshiteru Katsumura - 2016 - Transactions of the Japanese Society for Artificial Intelligence 31 (2):L-D43_1-9.
  33. Bayesianism and Inference to the Best Explanation.Valeraino Iranzo - unknown
    Bayesianism and Inference to the best explanation are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretations of prior probabilities: “IBE-Bayesianism” and “frequentist-Bayesianism”. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: endorses a role for explanatory value in the assessment of scientific hypotheses; (...)
  34. Bayesianism and Inference to the Best Explanation.Valeriano Iranzo - 2008 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 23 (1):89-106.
    Bayesianism and Inference to the best explanation are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretationsof prior probabilities: “IBE-Bayesianism” and “frequentist-Bayesianism”. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: endorses a role for explanatory value in the assessment of scientific hypotheses; avoids (...)
  35. Markov Chain Monte Carlo for Bayesian Inference Via Propositionalized Probability Computation.Masakazu Ishihata & Taisuke Sato - 2013 - Transactions of the Japanese Society for Artificial Intelligence 28 (2):230-242.
  36. Causal Origin and Evidence.Frank Jackson & Robert Pargetter - 1985 - Theoria 51 (2):65-76.
  37. The Intuitive Inadequacy of Classical Statistics.E. T. Jaynes - 1984 - Epistemologia 7 (43):43-74.
  38. Probability Reparation: The Problem of New Explanation. [REVIEW]Richard Jeffrey - 1995 - Philosophical Studies 77 (1):97 - 101.
  39. Dogmatism, Probability, and Logical Uncertainty.David Jehle & Brian Weatherson - 2012 - In Greg Restall & Gillian Kay Russell (eds.), New Waves in Philosophical Logic. Palgrave-Macmillan. pp. 95--111.
    Many epistemologists hold that an agent can come to justifiably believe that p is true by seeing that it appears that p is true, without having any antecedent reason to believe that visual impressions are generally reliable. Certain reliabilists think this, at least if the agent’s vision is generally reliable. And it is a central tenet of dogmatism (as described by Pryor (2000) and Pryor (2004)) that this is possible. Against these positions it has been argued (e.g. by Cohen (2005) (...)
  40. The Coherence Hypothesis: Critical Reconsideration, Reception History and Development of a Theoretical Model.Florian Jeserich * - 2014 - Archive for the Psychology of Religion 36 (1):1-51.
  41. Data-Based Construction of Multidimensional Probabilistic Models with MUDIM.Radim Jiroušek - 2006 - Logic Journal of the IGPL 14 (3):501-520.
    The goal of the paper is to introduce a program system, MUDIM, and to show how it can be used for multidimensional probabilistic model construction. The system is being developed with the goal to gain a tool for experimental computations with compositional models which are, in a way, an alternative to Bayesian networks. These models are based on the idea of composing a multidimensional distribution from a great number of low-dimensional ones. When considering knowledge-based systems, this approach quite naturally cope (...)
  42. Models and Statistical Inference: The Controversy Between Fisher and Neyman–Pearson.Lenhard Johannes - 2006 - British Journal for the Philosophy of Science 57 (1):69.
  43. Naive Probability: A Mental Model Theory of Extensional Reasoning.Philip Johnson-Laird, Paolo Legrenzi, Vittorio Girotto, Maria Sonino Legrenzi & Jean-Paul Caverni - 1999 - Psychological Review 106 (1):62-88.
    This article outlines a theory of naive probability. According to the theory, individuals who are unfamiliar with the probability calculus can infer the probabilities of events in an extensional way: They construct mental models of what is true in the various possibilities. Each model represents an equiprobable alternative unless individuals have beliefs to the contrary, in which case some models will have higher probabilities than others. The probability of an event depends on the proportion of models in which it occurs. (...)
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  44. Scientific Consistency, Two-Stage Priors and the True Value of a Parameter.Gareth Jones - 1982 - British Journal for the Philosophy of Science 33 (2):133-160.
  45. A Comparison of Two Methods of Event Randomization in Probability Learning.Mari R. Jones & Jerome L. Myers - 1966 - Journal of Experimental Psychology 72 (6):909.
  46. The Inverse Conjunction Fallacy.Martin Jönsson & James A. Hampton - unknown
    If people believe that some property is true of all members of a class such as sofas, then they should also believe that the same property is true of all members of a conjunctively defined subset of that class such as uncomfortable handmade sofas. A series of experiments demonstrated a failure to observe this constraint, leading to what is termed the inverse conjunction fallacy. Not only did people often express a belief in the more general statement but not in the (...)
  47. Deductive Coherence and Norm Adoption.Sindhu Joseph, Carles Sierra, Marco Schorlemmer & Pilar Dellunde - 2010 - Logic Journal of the IGPL 18 (1):118-156.
    This paper is a contribution to the formalisation of Thagard’s coherence theory. The term coherence is defined as the quality or the state of cohering, especially a logical, orderly, and aesthetically consistent relationship of parts. A coherent set is interdependent such that every element in it contributes to the coherence. We take Thagard’s proposal of a coherence set as that of maximising satisfaction of constraints between elements and explore its use in normative multiagent systems. In particular, we interpret coherence maximisation (...)
  48. The Value of Truth: A Reply to Howson.James M. Joyce - 2015 - Analysis 75 (3):413-424.
    Colin Howson has recently argued that accuracy arguments for probabilism fail because they assume a privileged ‘coding’ in which TRUE is assigned the value 1 and FALSE is assigned the value 0. I explain why this is wrong by first showing that Howson’s objections are based on a misconception about the way in which degrees of confidence are measured, and then reformulating the accuracy argument in a way that manifestly does not depend on the coding of truth-values. Along the way, (...)
  49. Commentary on Hartmann's Paper.E. Jurkowitz - 2008 - Boston Studies in the Philosophy of Science 255:131.
  50. Rethinking the Foundations of Statistics.Joseph B. Kadane, Mark J. Schervish & Teddy Seidenfeld - 1999 - Cambridge University Press.
    This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic of the collection is understanding how the norms for Bayesian decision making should apply in settings with more than one rational decision maker and then tracing out some of the consequences of this turn for Bayesian statistics. There are four principal themes to the collection: cooperative, non-sequential decisions; the representation and measurement of 'partially ordered' preferences; non-cooperative, sequential decisions; and pooling (...)
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