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  1. Peter Achinstein (1963). Confirmation Theory, Order, and Periodicity. Philosophy of Science 30 (1):17-35.
    This paper examines problems of order and periodicity which arise when the attempt is made to define a confirmation function for a language containing elementary number theory as applied to a universe in which the individuals are considered to be arranged in some fixed order. Certain plausible conditions of adequacy are stated for such a confirmation function. By the construction of certain types of predicates, it is proved, however, that these conditions of adequacy are violated by any confirmation function defined (...)
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  2. Peter Achinstein (1963). Variety and Analogy in Confirmation Theory. Philosophy of Science 30 (3):207-221.
    Confirmation theorists seek to define a function that will take into account the various factors relevant in determining the degree to which an hypothesis is confirmed by its evidence. Among confirmation theorists, only Rudolf Carnap has constructed a system which purports to consider factors in addition to the number of instances, viz. the variety manifested by the instances and the amount of analogy between the instances. It is the purpose of this paper to examine the problem which these additional factors (...)
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  3. Daniel Acuna & Paul Schrater (2008). Bayesian Modeling of Human Sequential Decision-Making on the Multi-Armed Bandit Problem. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. 100--200.
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  4. Ernest W. Adams (1988). Confirming Inexact Generalizations. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988:10 - 16.
    I suppose that 'ravens are black' is an inexact generalization having a degree of truth measured by the proportion of ravens that are black, and a probability measured by its expected degree of truth in different 'possible worlds.' Given this, 'ravens are black' differs in truth, probability, and confirmation from 'non-black things are not ravens', and this suggests a new approach to Hempel's Paradox as well as to other aspects of confirmation. Basic concepts of a formal theory developing this approach (...)
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  5. P. M. Ainsworth (2012). In Defence of Objective Bayesianism. Analysis 72 (4):832-843.
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  6. Jean Aitchison (1995). Free or Ensnared? The Hidden Nets Of. In E. Barker (ed.), Lse on Freedom. Lse Books. 75.
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  7. Laith Al-Shawaf & David Buss (2011). Evolutionary Psychology and Bayesian Modeling. Behavioral and Brain Sciences 34 (4):188-189.
    The target article provides important theoretical contributions to psychology and Bayesian modeling. Despite the article's excellent points, we suggest that it succumbs to a few misconceptions about evolutionary psychology (EP). These include a mischaracterization of evolutionary psychology's approach to optimality; failure to appreciate the centrality of mechanism in EP; and an incorrect depiction of hypothesis testing. An accurate characterization of EP offers more promise for successful integration with Bayesian modeling.
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  8. Casper J. Albers, Barteld P. Kooi & Willem Schaafsma (2005). Trying to Resolve the Two-Envelope Problem. Synthese 145 (1):89 - 109.
    After explaining the well-known two-envelope paradox by indicating the fallacy involved, we consider the two-envelope problem of evaluating the factual information provided to us in the form of the value contained by the envelope chosen first. We try to provide a synthesis of contributions from economy, psychology, logic, probability theory (in the form of Bayesian statistics), mathematical statistics (in the form of a decision-theoretic approach) and game theory. We conclude that the two-envelope problem does not allow a satisfactory solution. An (...)
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  9. Max Albert (2005). Should Bayesians Bet Where Frequentists Fear to Tread? Philosophy of Science 72 (4):584-593.
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  10. Max Albert (2003). Bayesian Rationality and Decision Making: A Critical Review. Analyse and Kritik 25 (1):101-117.
    Bayesianism is the predominant philosophy of science in North-America, the most important school of statistics world-wide, and the general version of the rational-choice approach in the social sciences. Although often rejected as a theory of actual behavior, it is still the benchmark case of perfect rationality. The paper reviews the development of Bayesianism in philosophy, statistics and decision making and questions its status as an account of perfect rationality. Bayesians, who otherwise are squarely in the empiricist camp, invoke a priori (...)
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  11. J. McKenzie Alexander (2009). Social Deliberation: Nash, Bayes, and the Partial Vindication of Gabriele Tarde. Episteme 6 (2):164-184.
    At the very end of the 19th century, Gabriele Tarde wrote that all society was a product of imitation and innovation. This view regarding the development of society has, to a large extent, fallen out of favour, and especially so in those areas where the rational actor model looms large. I argue that this is unfortunate, as models of imitative learning, in some cases, agree better with what people actually do than more sophisticated models of learning. In this paper, I (...)
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  12. Moorad Alexanian (forthcoming). Nature, Science, Bayes 'Theorem, and the Whole of Reality‖. Zygon.
    A fundamental problem in science is how to make logical inferences from scientific data. Mere data does not suffice since additional information is necessary to select a domain of models or hypotheses and thus determine the likelihood of each model or hypothesis. Thomas Bayes’ Theorem relates the data and prior information to posterior probabilities associated with differing models or hypotheses and thus is useful in identifying the roles played by the known data and the assumed prior information when making inferences. (...)
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  13. Nilufa Ali, Anne Schlottman, Abigail Shaw, Nick Chater, & Oaksford & Mike (2010). Causal Discounting and Conditional Reasoning in Children. In Mike Oaksford & Nick Chater (eds.), Cognition and Conditionals: Probability and Logic in Human Thinking. Oup Oxford.
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  14. Nilufa Ali, Anne Schlottmann, Abigail Shaw, Nick Chater & Mike Oaksford (2010). Causal Discounting and Conditional Reasoning in Children. In M. Oaksford & N. Chater (eds.), Cognition and Conditionals: Probability and Logic in Human Thought. Oxford University Press.
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  15. Ronald J. Allen (2001). Artificial Intelligence and the Evidentiary Process: The Challenges of Formalism and Computation. [REVIEW] Artificial Intelligence and Law 9 (2-3):99-114.
    The tension between rule and judgment is well known with respect to the meaning of substantive legal commands. The same conflict is present in fact finding. The law penetrates to virtually all aspects of human affairs; irtually any interaction can generate a legal conflict. Accurate fact finding about such disputes is a necessary condition for the appropriate application of substantive legal commands. Without accuracy in fact finding, the law is unpredictable, and thus individuals cannot efficiently accommodate their affairs to its (...)
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  16. Mair Allen-Williams & Nicholas R. Jennings (2009). Bayesian Learning for Cooperation in Multi-Agent Systems. In L. Magnani (ed.), Computational Intelligence. 321--360.
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  17. Peter Allmark (2005). Bayes and Health Care Research. Medicine, Health Care and Philosophy 7 (3):321-332.
    Bayes’ rule shows how one might rationally change one’s beliefs in the light of evidence. It is the foundation of a statistical method called Bayesianism. In health care research, Bayesianism has its advocates but the dominant statistical method is frequentism. There are at least two important philosophical differences between these methods. First, Bayesianism takes a subjectivist view of probability (i.e. that probability scores are statements of subjective belief, not objective fact) whilst frequentism takes an objectivist view. Second, Bayesianism is explicitly (...)
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  18. Sarah Allred (2012). Approaching Color with Bayesian Algorithms. In Gary Hatfield & Sarah Allred (eds.), Visual Experience: Sensation, Cognition, and Constancy. Oup Oxford. 212.
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  19. Paul Anand (2005). Bayes's Theorem (Proceedings of the British Academy, Vol. 113), Edited by Richard Swinburne, Oxford University Press, 2002, 160 Pages. [REVIEW] Economics and Philosophy 21 (1):139-142.
  20. Barton L. Anderson (2011). The Myth of Computational Level Theory and the Vacuity of Rational Analysis. Behavioral and Brain Sciences 34 (4):189-190.
    I extend Jones & Love's (J&L's) critique of Bayesian models and evaluate the conceptual foundations on which they are built. I argue that: (1) the part of Bayesian models is scientifically trivial; (2) theory is a fiction that arises from an inappropriate programming metaphor; and (3) the real scientific problems lie outside Bayesian theorizing.
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  21. Arthur Isak Applbaum (2014). Bayesian Inference and Contractualist Justification on Interstate 95. In Andrew I. Cohen & Christopher H. Wellman (eds.), Contemporary Debates in Applied Ethics. Wiley Blackwell. 219.
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  22. Horacio Arló-Costa (2001). Bayesian Epistemology and Epistemic Conditionals: On the Status of the Export-Import Laws. Journal of Philosophy 98 (11):555-593.
    Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.
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  23. Horacio Arló-Costa (2001). Bayesian Epistemology and Epistemic Conditionals. Journal of Philosophy 98 (11):555-593.
    The notion of probability occupies a central role in contemporary epistemology and cognitive science. Nevertheless, the classical notion of probability is hard to reconcile with the central notions postulated by the epistemological tradition.
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  24. Frank Arntzenius, Adam Elga & and John Hawthorne (2004). Bayesianism, Infinite Decisions, and Binding. Mind 113 (450):251-283.
    We pose and resolve several vexing decision theoretic puzzles. Some are variants of existing puzzles, such as ‘Trumped’ (Arntzenius and McCarthy 1997), ‘Rouble trouble’ (Arntzenius and Barrett 1999), ‘The airtight Dutch book’ (McGee 1999), and ‘The two envelopes puzzle’ (Broome 1999). Others are new. A unified resolution of the puzzles shows that Dutch book arguments have no force in infinite cases. It thereby provides evidence that reasonable utility functions may be unbounded and that reasonable credence functions need not be countably (...)
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  25. Jerrold L. Aronson (1989). The Bayesians and the Raven Paradox. Noûs 23 (2):221-240.
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  26. David Atkinson & Jeanne Peijnenburg (2008). Reichenbach's Posits Reposited. Erkenntnis 69 (1):93 - 108.
    Reichenbach’s use of ‘posits’ to defend his frequentistic theory of probability has been criticized on the grounds that it makes unfalsifiable predictions. The justice of this criticism has blinded many to Reichenbach’s second use of a posit, one that can fruitfully be applied to current debates within epistemology. We show first that Reichenbach’s alternative type of posit creates a difficulty for epistemic foundationalists, and then that its use is equivalent to a particular kind of Jeffrey conditionalization. We conclude that, under (...)
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  27. J. Austerweil & T. Griffiths (2008). A Rational Analysis of Confirmation with Deterministic Hypotheses. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. 1041--1046.
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  28. Joseph L. Austerweil & Thomas L. Griffiths (2011). Seeking Confirmation Is Rational for Deterministic Hypotheses. Cognitive Science 35 (3):499-526.
    The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best-known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. (...)
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  29. Peter C. Austin (2009). Are (the Log‐Odds of) Hospital Mortality Rates Normally Distributed? Implications for Studying Variations in Outcomes of Medical Care. Journal of Evaluation in Clinical Practice 15 (3):514-523.
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  30. Peter C. Austin, Lawrence J. Brunner & Janet E. Hux Md Sm (2002). Bayeswatch: An Overview of Bayesian Statistics. Journal of Evaluation in Clinical Practice 8 (2):277-286.
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  31. Peter C. Austin, Lawrence J. Brunner & E. Janet (2002). Bayeswatch: An Overview of Bayesian Statistics. Journal of Evaluation in Clinical Practice 8 (2):277-286.
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  32. Peter C. Austin, C. David Naylor & Jack V. Tu (2001). A Comparison of a Bayesian Vs. A Frequentist Method for Profiling Hospital Performance. Journal of Evaluation in Clinical Practice 7 (1):35-45.
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  33. Bengt Autzen (2011). Constraining Prior Probabilities of Phylogenetic Trees. Biology and Philosophy 26 (4):567-581.
    Although Bayesian methods are widely used in phylogenetic systematics today, the foundations of this methodology are still debated among both biologists and philosophers. The Bayesian approach to phylogenetic inference requires the assignment of prior probabilities to phylogenetic trees. As in other applications of Bayesian epistemology, the question of whether there is an objective way to assign these prior probabilities is a contested issue. This paper discusses the strategy of constraining the prior probabilities of phylogenetic trees by means of the Principal (...)
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  34. A. J. Ayer (1972). Probability and Evidence. [London]Macmillan.
  35. Theodore Bach (2014). A Unified Account of General Learning Mechanisms and Theory‐of‐Mind Development. Mind and Language 29 (3):351-381.
    Modularity theorists have challenged that there are, or could be, general learning mechanisms that explain theory-of-mind development. In response, supporters of the ‘scientific theory-theory’ account of theory-of-mind development have appealed to children's use of auxiliary hypotheses and probabilistic causal modeling. This article argues that these general learning mechanisms are not sufficient to meet the modularist's challenge. The article then explores an alternative domain-general learning mechanism by proposing that children grasp the concept belief through the progressive alignment of relational structure that (...)
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  36. Andrew Backe (1999). The Likelihood Principle and the Reliability of Experiments. Philosophy of Science 66 (3):361.
    The likelihood principle of Bayesian statistics implies that information about the stopping rule used to collect evidence does not enter into the statistical analysis. This consequence confers an apparent advantage on Bayesian statistics over frequentist statistics. In the present paper, I argue that information about the stopping rule is nevertheless of value for an assessment of the reliability of the experiment, which is a pre-experimental measure of how well a contemplated procedure is expected to discriminate between hypotheses. I show that, (...)
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  37. Irina Baetu, Itxaso Barberia, Robin A. Murphy & A. G. Baker (2011). Maybe This Old Dinosaur Isn't Extinct: What Does Bayesian Modeling Add to Associationism? Behavioral and Brain Sciences 34 (4):190-191.
    We agree with Jones & Love (J&L) that much of Bayesian modeling has taken a fundamentalist approach to cognition; but we do not believe in the potential of Bayesianism to provide insights into psychological processes. We discuss the advantages of associative explanations over Bayesian approaches to causal induction, and argue that Bayesian models have added little to our understanding of human causal reasoning.
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  38. Giosuè Baggio, Michiel Lambalgen & Peter Hagoort (2014). Logic as Marr's Computational Level: Four Case Studies. Topics in Cognitive Science 7 (1).
    We sketch four applications of Marr's levels-of-analysis methodology to the relations between logic and experimental data in the cognitive neuroscience of language and reasoning. The first part of the paper illustrates the explanatory power of computational level theories based on logic. We show that a Bayesian treatment of the suppression task in reasoning with conditionals is ruled out by EEG data, supporting instead an analysis based on defeasible logic. Further, we describe how results from an EEG study on temporal prepositions (...)
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  39. Patricia Baillie (1969). That Confirmation May yet Be a Probability. British Journal for the Philosophy of Science 20 (1):41-51.
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  40. Arnold Baise (2011). Objective Bayesian Probability. Libertarian Papers 3.
    The objective theory of probability of Richard von Mises has been criticized by Crovelli , who defends a subjective approach. This paper attempts to clarify the different meanings of ‘objective’ and ‘subjective’ when applied to probability, and then argues for an objective Bayesian theory of probability, as exemplified in the writings of E. T. Jaynes. In addition, a definition of probability based on this approach is given.
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  41. Max Baker-Hytch & Matthew A. Benton (forthcoming). Defeatism Defeated. Philosophical Perspectives.
    Many epistemologists are enamored with a defeat condition on knowledge. In this paper we present some implementation problems for defeatism, understood along either internalist or externalist lines. We then propose that one who accepts a knowledge norm of belief, according to which one ought to believe only what one knows, can explain away much of the motivation for defeatism. This is an important result, because on the one hand it respects the plausibility of the intuitions about defeat shared by many (...)
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  42. A. Balakrishnan & A. K. Shanmukham (1968). Equipment for Calibration of Bathythermograph and Preparation of Grids. In Peter Koestenbaum (ed.), Proceedings. [San Jose? Calif.. 38--334.
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  43. Alexandru Baltag & Sonja Smets (2008). Probabilistic Dynamic Belief Revision. Synthese 165 (2):179 - 202.
    We investigate the discrete (finite) case of the Popper–Renyi theory of conditional probability, introducing discrete conditional probabilistic models for knowledge and conditional belief, and comparing them with the more standard plausibility models. We also consider a related notion, that of safe belief, which is a weak (non-negatively introspective) type of “knowledge”. We develop a probabilistic version of this concept (“degree of safety”) and we analyze its role in games. We completely axiomatize the logic of conditional belief, knowledge and safe belief (...)
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  44. Greg Bamford (1999). What is the Problem of Ad Hoc Hypotheses? Science and Education 8 (4):375 - 86..
    The received view of an ad hochypothesis is that it accounts for only the observation(s) it was designed to account for, and so non-ad hocness is generally held to be necessary or important for an introduced hypothesis or modification to a theory. Attempts by Popper and several others to convincingly explicate this view, however, prove to be unsuccessful or of doubtful value, and familiar and firmer criteria for evaluating the hypotheses or modified theories so classified are characteristically available. These points (...)
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  45. Prasanta S. Bandyopadhayay, Robert J. Boik & Prasun Basu (1996). The Curve Fitting Problem: A Bayesian Approach. Philosophy of Science 63 (3):272.
    In the curve fitting problem two conflicting desiderata, simplicity and goodness-of-fit, pull in opposite directions. To this problem, we propose a solution that strikes a balance between simplicity and goodness-of-fit. Using Bayes' theorem we argue that the notion of prior probability represents a measurement of simplicity of a theory, whereas the notion of likelihood represents the theory's goodness-of-fit. We justify the use of prior probability and show how to calculate the likelihood of a family of curves. We diagnose the relationship (...)
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  46. Prasanta S. Bandyopadhyay & Robert J. Boik (1999). The Curve Fitting Problem: A Bayesian Rejoinder. Philosophy of Science 66 (3):402.
    In the curve fitting problem two conflicting desiderata, simplicity and goodness-of-fit pull in opposite directions. To solve this problem, two proposals, the first one based on Bayes's theorem criterion (BTC) and the second one advocated by Forster and Sober based on Akaike's Information Criterion (AIC) are discussed. We show that AIC, which is frequentist in spirit, is logically equivalent to BTC, provided that a suitable choice of priors is made. We evaluate the charges against Bayesianism and contend that AIC approach (...)
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  47. Prasanta S. Bandyopadhyay & Malcolm Forster (eds.) (forthcoming). Philosophy of Statistics, Handbook of the Philosophy of Science, Volume 7. Elsevier.
  48. Jean Baratgin & Guy Politzer (2011). Updating: A Psychologically Basic Situation of Probability Revision. Thinking and Reasoning 16 (4):253-287.
    The Bayesian model has been used in psychology as the standard reference for the study of probability revision. In the first part of this paper we show that this traditional choice restricts the scope of the experimental investigation of revision to a stable universe. This is the case of a situation that, technically, is known as focusing. We argue that it is essential for a better understanding of human probability revision to consider another situation called updating (Katsuno & Mendelzon, 1992), (...)
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  49. Jean Baratgin & Guy Politzer (2007). The Psychology of Dynamic Probability Judgment: Order Effect, Normative Theories, and Experimental Methodology. Mind and Society 6 (1):53-66.
    The Bayesian model is used in psychology as the reference for the study of dynamic probability judgment. The main limit induced by this model is that it confines the study of revision of degrees of belief to the sole situations of revision in which the universe is static (revising situations). However, it may happen that individuals have to revise their degrees of belief when the message they learn specifies a change of direction in the universe, which is considered as changing (...)
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  50. Jean Baratgin & Guy Politzer (2006). Is the Mind Bayesian? The Case for Agnosticism. Mind and Society 5 (1):1-38.
    This paper aims to make explicit the methodological conditions that should be satisfied for the Bayesian model to be used as a normative model of human probability judgment. After noticing the lack of a clear definition of Bayesianism in the psychological literature and the lack of justification for using it, a classic definition of subjective Bayesianism is recalled, based on the following three criteria: an epistemic criterion, a static coherence criterion and a dynamic coherence criterion. Then it is shown that (...)
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