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.
Related categories

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  1. Some Aspects of Probability and Induction (I).Jonathan Bennett - 1956 - British Journal for the Philosophy of Science 7 (27):220-230.
  2. Some Aspects of Probability and Induction (II).Jonathan Bennett - 1956 - British Journal for the Philosophy of Science 7 (28):316-322.
  3. 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.
  4. Turning Norton’s Dome Against Material Induction.Richard Dawid - 2015 - Foundations of Physics 45 (9):1101-1109.
    John Norton has proposed a position of “material induction” that denies the existence of a universal inductive inference schema behind scientific reasoning. In this vein, Norton has recently presented a “dome scenario” based on Newtonian physics that, in his understanding, is at variance with Bayesianism. The present note points out that a closer analysis of the dome scenario reveals incompatibilities with material inductivism itself.
  5. Classification and Filtering of Spectra: A Case Study in Mineralogy.Clark Glymour - unknown
    The ability to identify the mineral composition of rocks and soils is an important tool for the exploration of geological sites. Even though expert knowledge is commonly used for this task, it is desirable to create automated systems with similar or better performance. For instance, NASA intends to design robots that are sufficiently autonomous to perform this task on planetary missions. Spectrometer readings provide one important source of data for identifying sites with minerals of interest. Reflectance spectrometers measure intensities of (...)
  6. 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.
  7. Adding Geologic Prior Knowledge to Bayesian Lithofluid Facies Estimation From Seismic Data.Ezequiel F. Gonzalez, Stephane Gesbert & Ronny Hofmann - 2016 - Interpretation 4 (3):SL1-SL8.
  8. “Explanation, Prediction, and Confirmation in the Social Sciences: Realm and Limits” (University of Amsterdam, 26–27 October 2009). [REVIEW]Wenceslao J. Gonzalez - 2010 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 41 (2):389-394.
  9. SWINBURNE, R.: "An Introduction to Confirmation Theory". [REVIEW]I. J. Good - 1976 - British Journal for the Philosophy of Science 27:289.
  10. The Paradox of Confirmation.I. J. Good - 1961 - British Journal for the Philosophy of Science 12 (45):63-64.
  11. Probability and the Weighing of Evidence.Isidore Jacob Good - 1950 - C. Griffin London.
  12. The Paradox of Confirmation.L. J. Good - 1960 - British Journal for the Philosophy of Science 11 (42):145-b-145.
  13. Error and Scientific Reasoning.Michael E. Gorman - 1989 - In Steve Fuller (ed.), The Cognitive Turn: Sociological and Psychological Perspectives on Science. Kluwer Academic Publishers.
  14. A Very Brief Measure of the Big-Five Personality Domains.Samuel D. Gosling, Peter J. Rentfrow & William B. Swann Jr - 2003 - Journal of Research in Personality 37 (6):504-528.
    When time is limited, researchers may be faced with the choice of using an extremely brief measure of the Big-Five personality dimensions or using no measure at all. To meet the need for a very brief measure, 5 and 10-item inventories were developed and evaluated. Although somewhat inferior to standard multi-item instruments, the instruments reached adequate levels in terms of: convergence with widely used Big-Five measures in self, observer, and peer reports, test–retest reliability, patterns of predicted external correlates, and convergence (...)
  15. New Aspects of the Probabilistic Evaluation of Hypotheses and Experience.Rainer Gottlob - 2000 - International Studies in the Philosophy of Science 14 (2):147 – 163.
    The probabilistic corroboration of two or more hypotheses or series of observations may be performed additively or multiplicatively . For additive corroboration (e.g. by Laplace's rule of succession), stochastic independence is needed. Inferences, based on overwhelming numbers of observations without unexplained counterinstances permit hyperinduction , whereby extremely high probabilities, bordering on certainty for all practical purposes may be achieved. For multiplicative corroboration, the error probabilities (1 - Pr) of two (or more) hypotheses are multiplied. The probabilities, obtained by reconverting the (...)
  16. Testing the Null Hypothesis and the Strategy and Tactics of Investigating Theoretical Models.David A. Grant - 1962 - Psychological Review 69 (1):54-61.
  17. On the Neyman-Pearson Theory of Testing.Spencer Graves - 1978 - British Journal for the Philosophy of Science 29 (1):1-23.
  18. Evaluation of Statistical Hypotheses Using Information Transmitted.James G. Greeno - 1970 - Philosophy of Science 37 (2):279-294.
    The main argument of this paper is that an evaluation of the overall explanatory power of a theory is less problematic and more relevant as an assessment of the state of knowledge than evaluation of statistical explanations of single occurrences in terms of likelihoods that are assigned to explananda.
  19. Rejoinder to Richard Swinburne's 'Second Reply to Grunbaum'.A. Grunbaum - 2005 - British Journal for the Philosophy of Science 56 (4):927-938.
  20. Differential Nets, Experiments and Reduction.Giulio Guerrieri - unknown
  21. 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.
  22. Bayes Rules All: On the Equivalence of Various Forms of Learning in a Probabilistic Setting.Balazs Gyenis - unknown
    Jeffrey conditioning is said to provide a more general method of assimilating uncertain evidence than Bayesian conditioning. We show that Jeffrey learning is merely a particular type of Bayesian learning if we accept either of the following two observations: – Learning comprises both probability kinematics and proposition kinematics. – What can be updated is not the same as what can do the updating; the set of the latter is richer than the set of the former. We address the problem of (...)
  23. Causal Inferences in Nonexperimental Research.H. M. Blalock Jr - 1961
  24. Logic of Statistical Inference.Hacking Ian - 2016 - Cambridge University Press.
    One of Ian Hacking's earliest publications, this book showcases his early ideas on the central concepts and questions surrounding statistical reasoning. He explores the basic principles of statistical reasoning and tests them, both at a philosophical level and in terms of their practical consequences for statisticians. Presented in a fresh twenty-first-century series livery, and including a specially commissioned preface written by Jan-Willem Romeijn, illuminating its enduring importance and relevance to philosophical enquiry, Hacking's influential and original work has been revived for (...)
  25. Telepathy: Origins of Randomization in Experimental Design.Ian Hacking - 1988 - Isis 79 (3):427-451.
  26. Likelihood. [REVIEW]Ian Hacking - 1972 - British Journal for the Philosophy of Science 23 (2):132-137.
  27. Review: Likelihood. [REVIEW]Ian Hacking - 1972 - British Journal for the Philosophy of Science 23 (2):132 - 137.
  28. Slightly More Realistic Personal Probability.Ian Hacking - 1967 - Philosophy of Science 34 (4):311-325.
    A person required to risk money on a remote digit of π would, in order to comply fully with the theory [of personal probability] have to compute that digit, though this would really be wasteful if the cost of computation were more than the prize involved. For the postulates of the theory imply that you should behave in accordance with the logical implications of all that you know. Is it possible to improve the theory in this respect, making allowance within (...)
  29. On the Foundations of Statistics.Ian Hacking - 1964 - British Journal for the Philosophy of Science 15 (57):1-26.
  30. Categorical Induction From Uncertain Premises: Jeffrey's Doesn't Completely Rule.Constantinos Hadjichristidis, Steven A. Sloman & David E. Over - 2014 - Thinking and Reasoning 20 (4):405-431.
    Studies of categorical induction typically examine how belief in a premise (e.g., Falcons have an ulnar artery) projects on to a conclusion (e.g., Robins have an ulnar artery). We study induction in cases in which the premise is uncertain (e.g., There is an 80% chance that falcons have an ulnar artery). Jeffrey's rule is a normative model for updating beliefs in the face of uncertain evidence. In three studies we tested the descriptive validity of Jeffrey's rule and a related probability (...)
  31. Rational Argument, Rational Inference.Ulrike Hahn, Adam J. L. Harris & Mike Oaksford - 2013 - Argument and Computation 4 (1):21 - 35.
    (2013). Rational argument, rational inference. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 21-35. doi: 10.1080/19462166.2012.689327.
  32. Hierarchical Holographic Modeling for Conflict Resolution.Y. Y. Haimes & A. Weiner - 1986 - Philosophy of Science 53 (2):200-222.
  33. Interpretations of Probability.Alan Hájek - 2008 - Stanford Encyclopedia of Philosophy.
  34. Evidence with Uncertain Likelihoods.Joseph Halpern & Riccardo Pucella - 2009 - Synthese 171 (1):111-133.
    An agent often has a number of hypotheses, and must choose among them based on observations, or outcomes of experiments. Each of these observations can be viewed as providing evidence for or against various hypotheses. All the attempts to formalize this intuition up to now have assumed that associated with each hypothesis h there is a likelihood function μ h , which is a probability measure that intuitively describes how likely each observation is, conditional on h being the correct hypothesis. (...)
  35. Swans, Ravens, Death and Tyranny: On the Mythology of Freedom.Wendy C. Hamblet - 2009 - Philosophical Frontiers: A Journal of Emerging Thought 4 (2).
  36. Starting and Stopping.C. L. Hamblin - 1969 - The Monist 53 (3):410-425.
  37. Statistics in Physical Science.Walter Clark Hamilton - 1964 - New York: Ronald Press Co..
  38. Chapter 7. Knowledge and Probability.Gilbert Harman - 2015 - In Thought. Princeton University Press. pp. 112-125.
  39. Problems with Probabilistic Semantics.Gilbert Harman - 1983 - In Alex Orenstein & Rafael Stern (eds.), Developments in Semantics. Haven. pp. 243-237.
  40. Detachment, Probability, and Maximum Likelihood.Gilbert Harman - 1967 - Noûs 1 (4):401-411.
  41. Methodology.Richard J. Harris - unknown
    Despite publication of many well-argued critiques of null hypothesis testing (NHT), behavioral science researchers continue to rely heavily on this set of practices. Although we agree with most critics' catalogs of NHT's flaws, this article also takes the unusual stance of identifying virtues that may explain why NHT continues to be so extensively used. These virtues include providing results in the form of a dichotomous (yes/no) hypothesis evaluation and providing an index (p value) that has a justifiable mapping onto confidence (...)
  42. A New Garber-Style Solution to the Problem of Old Evidence.Stephan Hartmann & Branden Fitelson - 2015 - Philosophy of Science 82 (4):712-717.
    In this discussion note, we explain how to relax some of the standard assumptions made in Garber-style solutions to the Problem of Old Evidence. The result is a more general and explanatory Bayesian approach.
  43. 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.
  44. Probabilistic Model Building Genetic Programming Based on Estimation of Bayesian Network.Yoshihiko Hasegawa & Hitoshi Iba - 2007 - Transactions of the Japanese Society for Artificial Intelligence 22:37-47.
  45. Bayesian Inversion of Gravimetric Data and Assessment of CO2dissolution in the Utsira Formation.Vera Louise Hauge & Odd Kolbjørnsen - 2015 - Interpretation 3 (2):SP1-SP10.
  46. Modularity and the Causal Markov Condition: A Restatement.Daniel M. Hausman & James Woodward - 2004 - British Journal for the Philosophy of Science 55 (1):147-161.
    expose some gaps and difficulties in the argument for the causal Markov condition in our essay ‘Independence, Invariance and the Causal Markov Condition’ ([1999]), and we are grateful for the opportunity to reformulate our position. In particular, Cartwright disagrees vigorously with many of the theses we advance about the connection between causation and manipulation. Although we are not persuaded by some of her criticisms, we shall confine ourselves to showing how our central argument can be reconstructed and to casting doubt (...)
  47. Causation and Bayesian Networks-Manipulation and the Causal Markov Condition.Daniel Hausman & James Woodward - 2004 - Philosophy of Science 71 (5):846-856.
  48. Manipulation and the Causal Markov Condition.Daniel Hausman & James Woodward - 2004 - Philosophy of Science 71 (5):846-856.
    This paper explores the relationship between a manipulability conception of causation and the causal Markov condition (CM). We argue that violations of CM also violate widely shared expectations—implicit in the manipulability conception—having to do with the absence of spontaneous correlations. They also violate expectations concerning the connection between independence or dependence relationships in the presence and absence of interventions.
  49. Independence, Invariance and the Causal Markov Condition.DM Hausman & J. Woodward - 1999 - British Journal for the Philosophy of Science 50 (4):521-583.
    This essay explains what the Causal Markov Condition says and defends the condition from the many criticisms that have been launched against it. Although we are skeptical about some of the applications of the Causal Markov Condition, we argue that it is implicit in the view that causes can be used to manipulate their effects and that it cannot be surrendered without surrendering this view of causation.
  50. Earman, J.(Ed.)-Inference, Explanation, and Other Frustrations.A. Heathcote - 1996 - Philosophical Books 37:133-133.
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