Probabilistic Reasoning Edited by Jonathan Weisberg (University of Toronto)

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Bayesian Reasoning
  1. N. Hall (1999). How to Set a Surprise Exam. Mind 108 (432):647-703.
    The professor announces a surprise exam for the upcoming week; her clever student purports to demonstrate by reductio that she cannot possibly give such an exam. Diagnosing his puzzling argument reveals a deeper puzzle: Is the student justified in believing the announcement? It would seem so, particularly if the upcoming 'week' is long enough. On the other hand, a plausible principle states that if, at the outset, the student is justified in believing some proposition, then he is also justified in (...)
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  2. William W. Rozeboom (1968). New Dimensions of Confirmation Theory. Philosophy of Science 35 (2):134-155.
    When Hempel's "paradox of confirmation" is developed within the confines of conditional probability theory, it becomes apparent that two seemingly equivalent generalities ("laws") can have exactly the same class of observational refuters even when their respective classes of confirming observations are importantly distinct. Generalities which have the inductive supports we commonsensically construe them to have, however, must incorporate quasi-logical operators or connectives which cannot be defined truth-functionally. The origins and applications of these "modalic" concepts appear to be intimately linked with (...)
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Bayesian Reasoning, Misc
  1. Christophe Abraham & Jean-Pierre Daures (2000). Global Robustness with Respect to the Loss Function and the Prior. Theory and Decision 48 (4):359-381.
    We propose a class [I,S] of loss functions for modeling the imprecise preferences of the decision maker in Bayesian Decision Theory. This class is built upon two extreme loss functions I and S which reflect the limited information about the loss function. We give an approximation of the set of Bayes actions for every loss function in [I,S] and every prior in a mixture class; if the decision space is a subset of R, we obtain the exact set.
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  2. Alan Hájek & Stephan Hartmann, Bayesian Epistemology.
    According to one view, there cannot: Bayesianism fails to do justice to essential aspects of knowledge and belief, and as such it cannot provide a genuine epistemology at all. According to another view, Bayesianism should supersede traditional epistemology: where the latter has been mired in endless debates over skepticism and Gettierology, Bayesianism offers the epistemologist a thriving research program. We will advocate a more moderate view: Bayesianism can illuminate various long­standing problems of epistemology, while not addressing all of them; and (...)
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  3. Ronald J. Allen (2001). Artificial Intelligence and the Evidentiary Process: The Challenges of Formalism and Computation. Artificial Intelligence and Law 9 (2-3).
    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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  4. Paul Anand (2005). Bayes's Theorem (Proceedings of the British Academy, Vol. 113), Edited by Richard Swinburne, Oxford University Press, 2002, 160 Pages. Economics and Philosophy 21 (1):139-142.
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  5. Horacio Arló-Costa (2001). Bayesian Epistemology and Epistemic Conditionals: On the Status of the Export-Import Laws. Journal of Philosophy 98 (11):555-593.
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  6. Horacio Arlo-Costa, Bayesian Epistemology and Epistemic Conditionals: On the Status of the Export-Import Laws.
    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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  7. 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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  8. 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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  9. Arnold Baise, 20. “Objective Bayesian Probability”.
    The objective theory of probability of Richard von Mises has been criticized by Crovelli (2009), 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 [...].
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  10. 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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  11. 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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  12. 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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  13. 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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  14. 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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  15. Jeffrey A. Barrett (1996). Oracles, Aesthetics, and Bayesian Consensus. Philosophy of Science 63 (3):280.
    In order for Bayesian inquiry to count as objective, one might argue that it must lead to a consensus among those who use it and share evidence, but presumably this is not enough. It has been proposed that one should also require that the consensus be reached from very different initial opinions by conditioning only on basic experimental evidence, evidence free from subjective, social, or psychological influence. I will argue here, however, that this notion of objectivity in Bayesian inquiry is (...)
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  16. Paul Bartha & Christopher Hitchcock (1999). No One Knows the Date or the Hour: An Unorthodox Application of Rev. Bayes's Theorem. Philosophy of Science 66 (3):353.
    Carter and Leslie (1996) have argued, using Bayes's theorem, that our being alive now supports the hypothesis of an early 'Doomsday'. Unlike some critics (Eckhardt 1997), we accept their argument in part: given that we exist, our existence now indeed favors 'Doom sooner' over 'Doom later'. The very fact of our existence, however, favors 'Doom later'. In simple cases, a hypothetical approach to the problem of 'old evidence' shows that these two effects cancel out: our existence now yields no information (...)
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  17. Pierre Bessière (forthcoming). Common Bayesian Models for Common Cognitive Issues. Acta Biotheoretica.
    How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common (...)
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  18. Gregor Betz (2008). Evaluating Dialectical Structures with Bayesian Methods. Synthese 163 (1):25 - 44.
    This paper shows how complex argumentation, analyzed as dialectical structures, can be evaluated within a Bayesian framework by interpreting them as coherence constraints on subjective degrees of belief. A dialectical structure is a set of arguments (premiss-conclusion structure) among which support- and attack-relations hold. This approach addresses the observation that some theses in a debate can be better justified than others and thus fixes a shortcoming of a theory of defeasible reasoning which applies the bivalence principle to argument evaluations by (...)
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  19. Fernando Birman (2009). On the Rationality of Decisions with Unreliable Probabilities. Disputatio 26 (3):97-116.
    The standard Bayesian recipe for selecting the rational choice is presented. A familiar example in which the recipe fails to produce any definite result is introduced. It is argued that a generalization of Gärdenfors’ and Sahlin’s theory of unreliable probabilities — which itself does not guarantee a solution to the problem — offers the best available approach. But a number of challenges to this approach are also presented and discussed.
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  20. Allan Birnbaum (1977). The Neyman-Pearson Theory as Decision Theory, and as Inference Theory; with a Criticism of the Lindley-Savage Argument for Bayesian Theory. Synthese 36 (1):19 - 49.
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  21. Luc Bovens & Stephan Hartmann (2004). Bayesian Epistemology. OUP Oxford.
    Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information sources. (...)
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  22. Luc Bovens & Stephan Hartmann (2003). Bayesian Epistemology. Oxford: Oxford University Press.
    Bovens and Hartmann provide a systematic guide to the use of probabilistic methods not just in epistemology, but also in philosophy of science, voting theory, ...
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  23. Luc Bovens & Stephan Hartmann (2002). Bayesian Networks and the Problem of Unreliable Instruments. Philosophy of Science 69 (1):29-72.
    We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments. In particular, we consider (i) repeated measurements of a single test consequence of the hypothesis, (ii) measurements of multiple test consequences of the hypothesis, (iii) theoretical support for the reliability of the instrument, and (iv) calibration procedures. We evaluate these strategies on their relative merits under idealized conditions and show some surprising (...)
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  24. Luc Bovens & Stephan Hartmann (2000). Coherence, Belief Expansion and Bayesian Networks. In BaralC (ed.), Proceedings of the 8th International Workshop on Non-Monotonic Reasoning, NMR'2000.
    We construct a probabilistic coherence measure for information sets which determines a partial coherence ordering. This measure is applied in constructing a criterion for expanding our beliefs in the face of new information. A number of idealizations are being made which can be relaxed by an appeal to Bayesian Networks.
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  25. Luc Bovens & EJ Olsson (2000). Coherentism, Reliability and Bayesian Networks. Mind 109 (436):685-719.
    The coherentist theory of justification provides a response to the sceptical challenge: even though the independent processes by which we gather information about the world may be of dubious quality, the internal coherence of the information provides the justification for our empirical beliefs. This central canon of the coherence theory of justification is tested within the framework of Bayesian networks, which is a theory of probabilistic reasoning in artificial intelligence. We interpret the independence of the information gathering processes (IGPs) in (...)
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  26. Luc Bovens & Wlodek Rabinowicz (2010). The Puzzle of the Hats. Synthese 172 (1).
    The Puzzle of the Hats is a betting arrangement which seems to show that a Dutch book can be made against a group of rational players with common priors who act in the common interest and have full trust in the other players’ rationality. But we show that appearances are misleading—no such Dutch book can be made. There are four morals. First, what can be learned from the puzzle is that there is a class of situations in which credences and (...)
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  27. Darren Bradley (forthcoming). Weisberg on Design: What Fine-Tuning’s Got to Do with It. Erkenntnis:-.
    Abstract Jonathan Weisberg (Analysis, 70(3), pp. 431–438, 2010 ) argues that, given that life exists, the fact that the universe is fine-tuned for life does not confirm the design hypothesis. And if the fact that life exists confirms the design hypothesis, fine-tuning is irrelevant. So either way, fine-tuning has nothing to do with it. I will defend a design argument that survives Weisberg’s critique—the fact that life exists supports the design hypothesis, but it only does so given fine-tuning. Content Type (...)
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  28. Darren Bradley (2009). Multiple Universes and Observation Selection Effects. American Philosophical Quarterly 46 (1):2009.
    The fine-tuning argument can be used to support the Many Universe hypothesis. The Inverse Gambler’s Fallacy objection seeks to undercut the support for the Many Universe hypothesis. The objection is that although the evidence that there is life somewhere confirms Many Universes, the specific evidence that there is life in this universe does not. I will argue that the Inverse Gambler’s Fallacy is not committed by the fine-tuning argument. The key issue is the procedure by which the universe with life (...)
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  29. Darren Bradley (2009). Multiple Universes and Observation Selection Effects. American Philosophical Quarterly 46 (1):72.
    The fine-tuning argument can be used to support the Many Universe hypothesis. The Inverse Gambler’s Fallacy objection seeks to undercut the support for the Many Universe hypothesis. The objection is that although the evidence that there is life somewhere confirms Many Universes, the specific evidence that there is life in this universe does not. I will argue that the Inverse Gambler’s Fallacy is not committed by the fine-tuning argument. The key issue is the procedure by which the universe with life (...)
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  30. M. C. Bradley (2002). The Fine-Tuning Argument: The Bayesian Version. Religious Studies 38 (4):375-404.
    This paper considers the Bayesian form of the fine-tuning argument as advanced by Richard Swinburne. An expository section aims to identify the precise character of the argument, and three lines of objection are then advanced. The first of these holds that there is an inconsistency in Swinburne's procedure, the second that his argument has an unacceptable dependence on an objectivist theory of value, the third that his method is powerless to single out traditional theism from a vast number of (...)
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  31. Peter Bradley (2008). Constancy, Categories and Bayes: A New Approach to Representational Theories of Color Constancy. Philosophical Psychology 21 (5):601 – 627.
    Philosophers have long sought to explain perceptual constancy—the fact that objects appear to remain the same color, size and shape despite changes in the illumination condition, perspective and the relative distance—in terms of a mechanism that actively categorizes variable stimuli under the same pre-formed conceptual categories. Contemporary representationalists, on the other hand, explain perceptual constancy in terms of a modular mechanism that automatically discounts variation in the visual field to represent the stable properties of objects. In this paper I argue (...)
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  32. R. Bradley (2005). Bayesian Utilitarianism and Probability Homogeneity. Social Choice and Welfare 24:221-251.
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  33. Richard Bradley (2007). A Unified Bayesian Decision Theory. Theory and Decision 63:233-263,.
    This paper provides new foundations for Bayesian Decision Theory based on a representation theorem for preferences defined on a set of prospects containing both factual and conditional possibilities. This use of a rich set of prospects not only provides a framework within which the main theoretical claims of Savage, Ramsey, Jeffrey and others can be stated and compared, but also allows for the postulation of an extended Bayesian model of rational belief and desire from which they can be derived as (...)
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  34. Richard Bradley (2005). Radical Probabilism and Bayesian Conditioning. Philosophy of Science 72 (2):342-364.
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  35. Richard Bradley (2001). Ramsey and the Measurement of Belief. In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism.
    Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today. Some of these papers seek to clarify the relationships between Bayesian, causal and logical reasoning. Others consider the application of Bayesianism to artificial intelligence, decision theory, statistics and the philosophy of science and mathematics. The volume includes important criticisms of Bayesian reasoning and also gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. (...)
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  36. H. I. Brown (1994). Book Reviews : John Earman, Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory. MIT Press, Cambridge, MA, 1992. Pp. Xvi, 272. $35.00 (Cloth. Philosophy of the Social Sciences 24 (3):383-385.
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  37. Harold I. Brown (1994). Reason, Judgement and Bayes's Law. Philosophy of Science 61 (3):351-369.
    This paper argues that when used judiciously Bayes's law has a role to play in the evaluation of scientific hypotheses. Several examples are presented in which a rational response to evidence requires a judgement whether to apply Bayes's law or whether, for example, to redistribute prior probabilities. The paper concludes that reflection on Bayes's law illustrates how an adequate account of the rational evaluation of hypotheses requires an account of judgement--a point which several philosophers have noted despite few attempts to (...)
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  38. Lara Buchak (2010). Instrumental Rationality, Epistemic Rationality, and Evidence-Gathering. Philosophical Perspectives 24 (1):85-120.
  39. Richard Buxton (1978). The Interpretation and Justification of the Subjective Bayesian Approach to Statistical Inference. British Journal for the Philosophy of Science 29 (1):25-38.
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  40. Nancy Cartwright (2001). What Is Wrong With Bayes Nets? The Monist 84 (2).
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  41. Jake Chandler (forthcoming). The Transmission of Support: A Bayesian Re-Analysis. Synthese.
    Crispin Wright’s discussion of the notion of ‘transmission-failure’ promises to have important philosophical ramifications, both in epistemology and beyond. This paper offers a precise, formal characterisation of the concept within a Bayesian framework. The interpretation given avoids the serious shortcomings of a recent alternative proposal due to Samir Okasha.
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  42. Nick Chater & Mike Oaksford (2008). The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford.
    The rational analysis method, first proposed by John R. Anderson, has been enormously influential in helping us understand high-level cognitive processes. -/- 'The Probabilistic Mind' is a follow-up to the influential and highly cited 'Rational Models of Cognition' (OUP, 1998). It brings together developments in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods. It synthesizes and evaluates the progress in the past decade, taking into account developments in Bayesian (...)
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  43. Xiaoping Chen (2006). Bayesian Test and Kuhn's Paradigm. Frontiers of Philosophy in China 1 (3):491-505.
    Kuhn’s theory of paradigm reveals a pattern of scientific progress, in which normal science alternates with scientific revolution. But Kuhn underrated too much the function of scientific test in his pattern, because he focuses all his attention on the hypothetico-deductive schema instead of Bayesian schema. This paper employs Bayesian schema to re-examine Kuhn’s theory of paradigm, to uncover its logical and rational components, and to illustrate the tensional structure of logic and belief, rationality and irrationality, in the process of (...)
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  44. Charles S. Chihara (1987). Some Problems for Bayesian Confirmation Theory. British Journal for the Philosophy of Science 38 (4):551-560.
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  45. S. Choi (2006). Review: Bayesian Nets and Causality: Philosophical and Computational Foundations. Mind 115 (458):502-506.
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  46. David Christensen (2001). Preference-Based Arguments for Probabilism. Philosophy of Science 68 (3):356-376.
    Both Representation Theorem Arguments and Dutch Book Arguments support taking probabilistic coherence as an epistemic norm. Both depend on connecting beliefs to preferences, which are not clearly within the epistemic domain. Moreover, these connections are standardly grounded in questionable definitional/metaphysical claims. The paper argues that these definitional/metaphysical claims are insupportable. It offers a way of reconceiving Representation Theorem arguments which avoids the untenable premises. It then develops a parallel approach to Dutch Book Arguments, and compares the results. In each case (...)
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  47. David Christensen (1994). John Earman's 'Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory' (Book Review). Philosophical Review 103:345-347.
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  48. David Christensen (1992). Confirmational Holism and Bayesian Epistemology. Philosophy of Science 59 (4):540-557.
    Much contemporary epistemology is informed by a kind of confirmational holism, and a consequent rejection of the assumption that all confirmation rests on experiential certainties. Another prominent theme is that belief comes in degrees, and that rationality requires apportioning one's degrees of belief reasonably. Bayesian confirmation models based on Jeffrey Conditionalization attempt to bring together these two appealing strands. I argue, however, that these models cannot account for a certain aspect of confirmation that would be accounted for in any adequate (...)
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  49. M. Wayne Cooper (1992). Should Physicians Be Bayesian Agents? Theoretical Medicine and Bioethics 13 (4).
    Because physicians use scientific inference for the generalizations of individual observations and the application of general knowledge to particular situations, the Bayesian probability solution to the problem of induction has been proposed and frequently utilized. Several problems with the Bayesian approach are introduced and discussed. These include: subjectivity, the favoring of a weak hypothesis, the problem of the false hypothesis, the old evidence/new theory problem and the observation that physicians are not currently Bayesians. To the complaint that the prior probability (...)
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  50. Jukka Corander & Pekka Marttinen (2006). Bayesian Model Learning Based on Predictive Entropy. Journal of Logic, Language and Information 15 (1-2).
    Bayesian paradigm has been widely acknowledged as a coherent approach to learning putative probability model structures from a finite class of candidate models. Bayesian learning is based on measuring the predictive ability of a model in terms of the corresponding marginal data distribution, which equals the expectation of the likelihood with respect to a prior distribution for model parameters. The main controversy related to this learning method stems from the necessity of specifying proper prior distributions for all unknown parameters of (...)
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  51. David Corfield & Jon Williamson (2001). Foundations of Bayesianism. Kluwer Academic Publishers.
    The volume includes important criticisms of Bayesian reasoning and also gives an insight into some of the points of disagreement amongst advocates of the ...
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  52. Vincenzo Crupi, Roberto Festa & and Tommaso Mastropasqua (2008). Bayesian Confirmation by Uncertain Evidence: A Reply to Huber [2005]. British Journal for the Philosophy of Science 59 (2):201-211.
    Bayesian epistemology postulates a probabilistic analysis of many sorts of ordinary and scientific reasoning. Huber ([2005]) has provided a novel criticism of Bayesianism, whose core argument involves a challenging issue: confirmation by uncertain evidence. In this paper, we argue that under a properly defined Bayesian account of confirmation by uncertain evidence, Huber's criticism fails. By contrast, our discussion will highlight what we take as some new and appealing features of Bayesian confirmation theory. Introduction Uncertain Evidence and Bayesian Confirmation Bayesian Confirmation (...)
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  53. Vincenzo Crupi, Roberto Festa & Carlo Buttasi (2010). Toward a Grammar of Bayesian Confirmation. In M. Suàrez, M. Dorato & M. Redéi (eds.), EPSA Epistemology and Methodology of Science: Launch of the a European Philosophy of Science Association. Springer.
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  54. Vincenzo Crupi & Stephan Hartmann (2010). Formal and Empirical Methods in Philosophy of Science. In Friedrich Stadler et al (ed.), The Present Situation in the Philosophy of Science. Springer.
    This essay addresses the methodology of philosophy of science and illustrates how formal and empirical methods can be fruitfully combined. Special emphasis is given to the application of experimental methods to confirmation theory and to recent work on the conjunction fallacy, a key topic in the rationality debate arising from research in cognitive psychology. Several other issue can be studied in this way. In the concluding section, a brief outline is provided of three further examples.
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  55. Vincenzo Crupi, Katya Tentori & and Michel Gonzalez (2007). On Bayesian Measures of Evidential Support: Theoretical and Empirical Issues. Philosophy of Science 74 (2):229-252.
    Epistemologists and philosophers of science have often attempted to express formally the impact of a piece of evidence on the credibility of a hypothesis. In this paper we will focus on the Bayesian approach to evidential support. We will propose a new formal treatment of the notion of degree of confirmation and we will argue that it overcomes some limitations of the currently available approaches on two grounds: (i) a theoretical analysis of the confirmation relation seen as an extension of (...)
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  56. David Danks (2007). Reasons as Causes in Bayesian Epistemology. Journal of Philosophy 104 (9):464-474.
    In everyday matters, as well as in law, we allow that someone’s reasons can be causes of her actions, and often are. That correct reasoning accords with Bayesian principles is now so widely held in philosophy, psychology, computer science and elsewhere that the contrary is beginning to seem obtuse, or at best quaint. And that rational agents should learn about the world from energies striking sensory inputs nerves in people—seems beyond question. Even rats seem to recognize the difference between correlation (...)
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  57. David Danks & Clark Glymour, Linearity Properties of Bayes Nets with Binary Variables.
    It is “well known” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables” sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a (...)
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  58. Philip Dawid & Donald Gillies (1989). A Bayesian Analysis of Hume's Argument Concerning Miracles. Philosophical Quarterly 39 (154):57-65.
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  59. William Dembski, Design by Elimination Vs. Design by Comparison.
    Behind this question are two fundamentally different approaches about how to reason with chance hypotheses. One approach, due to Ronald Fisher, rejects a chance hypothesis provided sample data appear in a prespecified rejection region. The other, due to Thomas Bayes, rejects a chance hypothesis provided an alternative hypothesis confers a bigger probability on the data in question than the original hypothesis. In the Fisherian approach, chance hypotheses are rejected in isolation for rendering data too improbable. In the Bayesian approach, chance (...)
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  60. Persi Diaconis & Susan Holmes (1996). Are There Still Things to Do in Bayesian Statistics? Erkenntnis 45 (2-3):145 - 158.
    From the outside, Bayesian statistics may seem like a closed little corner of probability. Once a prior is specified you compute! From the inside the field is filled with problems, conceptual and otherwise. This paper surveys some of what remains to be done and gives examples of the work in progress via a Bayesian peek into Feller volume I.
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  61. Franz Dietrich, Bayesian Group Belief.
    If a group is modelled as a single Bayesian agent, what should its beliefs be? I propose an axiomatic model that connects group beliefs to beliefs of group members, who are themselves modelled as Bayesian agents, possibly with di¤erent priors and di¤erent information. Group beliefs are proven to take a simple multiplicative form if people’s information is independent, and a more complex form if information overlaps arbitrarily. This shows that group beliefs can incorporate all information spread over the individuals without (...)
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  62. Franz Dietrich & Luca Moretti (2005). On Coherent Sets and the Transmission of Confirmation. Philosophy of Science 73(3) 72 (3):403-424.
    In this paper, we identify a new and mathematically well-defined sense in which the coherence of a set of hypotheses can be truth-conducive. Our focus is not, as usually, on the probability but on the confirmation of a coherent set and its members. We show that, if evidence confirms a hypothesis, confirmation is "transmitted" to any hypotheses that are sufficiently coherent with the former hypothesis, according to some appropriate probabilistic coherence measure such as Olsson’s or Fitelson’s measure. Our findings have (...)
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  63. Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann (forthcoming). Confirmation and Reduction: A Bayesian Account. Synthese 179:2 (2011).
    Various scientific theories stand in a reductive relation to each other. In a recent article, we have argued that a generalized version of the Nagel-Schaffner model (GNS) is the right account of this relation. In this article, we present a Bayesian analysis of how GNS impacts on confirmation. We formalize the relation between the reducing and the reduced theory before and after the reduction using Bayesian networks, and thereby show that, post-reduction, the two theories are confirmatory of each other. We (...)
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  64. Frank Döring (1999). Why Bayesian Psychology is Incomplete. Philosophy of Science 66 (3):389.
    Bayesian psychology, in what is perhaps its most familiar version, is incomplete: Jeffrey conditionalization is not a complete account of rational belief change. Jeffrey conditionalization is sensitive to the order in which the evidence arrives. This order effect can be so pronounced as to call for a belief adjustment that cannot be understood as an assimilation of incoming evidence by Jeffrey's rule. Hartry Field's reparameterization of Jeffrey's rule avoids the order effect but fails as an account of how new evidence (...)
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  65. J. Dorling (1979). Bayesian Personalism, the Methodology of Scientific Research Programmes, and Duhem's Problem. Studies in History and Philosophy of Science Part A 10 (3):177-187.
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  66. Jon Dorling & David Miller (1981). Bayesian Personalism, Falsificationism, and the Problem of Induction. Aristotelian Society Supplementary Volume 55:109 - 141.
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  67. Igor Douven (2009). Assertion, Moore, and Bayes. Philosophical Studies 144 (3):361 - 375.
    It is widely believed that the so-called knowledge account of assertion best explains why sentences such as “It’s raining in Paris but I don’t believe it” and “It’s raining in Paris but I don’t know it” appear odd to us. I argue that the rival rational credibility account of assertion explains that fact just as well. I do so by providing a broadly Bayesian analysis of the said type of sentences which shows that such sentences cannot express rationally held beliefs. (...)
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  68. Igor Douven (2002). A New Solution to the Paradoxes of Rational Acceptability. British Journal for the Philosophy of Science 53 (3):391-410.
    The Lottery Paradox and the Preface Paradox both involve the thesis that high probability is sufficient for rational acceptability. The standard solution to these paradoxes denies that rational acceptability is deductively closed. This solution has a number of untoward consequences. The present paper suggests that a better solution to the paradoxes is to replace the thesis that high probability suffices for rational acceptability with a somewhat stricter thesis. This avoids the untoward consequences of the standard solution. The new solution will (...)
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  69. Igor Douven & Timothy Williamson (2006). Generalizing the Lottery Paradox. British Journal for the Philosophy of Science 57 (4):755-779.
    This paper is concerned with formal solutions to the lottery paradox on which high probability defeasibly warrants acceptance. It considers some recently proposed solutions of this type and presents an argument showing that these solutions are trivial in that they boil down to the claim that perfect probability is sufficient for rational acceptability. The argument is then generalized, showing that a broad class of similar solutions faces the same problem. An argument against some formal solutions to the lottery paradox The (...)
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  70. David L. Dowe (2008). Minimum Message Length and Statistically Consistent Invariant (Objective?) Bayesian Probabilistic Inference—From (Medical) “Evidence”. Social Epistemology 22 (4):433 – 460.
    “Evidence” in the form of data collected and analysis thereof is fundamental to medicine, health and science. In this paper, we discuss the “evidence-based” aspect of evidence-based medicine in terms of statistical inference, acknowledging that this latter field of statistical inference often also goes by various near-synonymous names—such as inductive inference (amongst philosophers), econometrics (amongst economists), machine learning (amongst computer scientists) and, in more recent times, data mining (in some circles). Three central issues to this discussion of “evidence-based” are (i) (...)
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  71. David L. Dowe, Steve Gardner & and Graham Oppy (2007). Bayes Not Bust! Why Simplicity Is No Problem for Bayesians. British Journal for the Philosophy of Science 58 (4):709 - 754.
    The advent of formal definitions of the simplicity of a theory has important implications for model selection. But what is the best way to define simplicity? Forster and Sober ([1994]) advocate the use of Akaike's Information Criterion (AIC), a non-Bayesian formalisation of the notion of simplicity. This forms an important part of their wider attack on Bayesianism in the philosophy of science. We defend a Bayesian alternative: the simplicity of a theory is to be characterised in terms of Wallace's Minimum (...)
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  72. David Dowe & Graham Oppy (2001). Universal Bayesian Inference? Behavioral and Brain Sciences 24 (4):662-663.
    We criticise Shepard's notions of “invariance” and “universality,” and the incorporation of Shepard's work on inference into the general framework of his paper. We then criticise Tenenbaum and Griffiths' account of Shepard (1987b), including the attributed likelihood function, and the assumption of “weak sampling.” Finally, we endorse Barlow's suggestion that minimum message length (MML) theory has useful things to say about the Bayesian inference problems discussed by Shepard and Tenenbaum and Griffiths. [Barlow; Shepard; Tenenbaum & Griffiths].
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  73. Antony Eagle (2005). Randomness Is Unpredictability. British Journal for the Philosophy of Science 56 (4):749 - 790.
    The concept of randomness has been unjustly neglected in recent philosophical literature, and when philosophers have thought about it, they have usually acquiesced in views about the concept that are fundamentally flawed. After indicating the ways in which these accounts are flawed, I propose that randomness is to be understood as a special case of the epistemic concept of the unpredictability of a process. This proposal arguably captures the intuitive desiderata for the concept of randomness; at least it should suggest (...)
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  74. J. Earman (1990). Bayes' Bayesianism. Studies in History and Philosophy of Science Part A 21 (3):351-370.
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  75. John Earman (1993). Bayes, Hume, and Miracles. Faith and Philosophy 10 (3):293-310.
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  76. Kenny Easwaran & Branden Fitelson (forthcoming). An 'Evidentialist' Worry About Joyce's Argument for Probabilism. Dialetica.
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  77. Frederick Eberhardt & David Danks (2011). Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. Minds and Machines 21 (3):389-410.
    Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the (...)
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  78. Ellery Eells (2004). Review: Bayes's Theorem. Mind 113 (451):591-596.
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  79. Ellery Eells & James Fetzer (forthcoming). The Place of Probability in Science. Springer.
    To clarify and illuminate the place of probability in science Ellery Eells and James H. Fetzer have brought together some of the most distinguished philosophers ...
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  80. Ellery Eells & Branden Fitelson (2000). Comments and Criticism: Measuring Confirmation and Evidence. Journal of Philosophy 97 (12):663-672.
    Bayesian epistemology suggests various ways of measuring the support that a piece of evidence provides a hypothesis. Such measures are defined in terms of a subjective probability assignment, pr, over propositions entertained by an agent. The most standard measure (where “H” stands for “hypothesis” and “E” stands for “evidence”) is: the difference measure: d(H,E) = pr(H/E) - pr(H).0 This may be called a “positive (probabilistic) relevance measure” of confirmation, since, according to it, a piece of evidence E qualitatively confirms a (...)
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  81. Andy Egan (2007). Some Counterexamples to Causal Decision Theory. Philosophical Review 116 (1):93-114.
    Many philosophers (myself included) have been converted to causal decision theory by something like the following line of argument: Evidential decision theory endorses irrational courses of action in a range of examples, and endorses “an irrational policy of managing the news”. These are fatal problems for evidential decision theory. Causal decision theory delivers the right results in the troublesome examples, and does not endorse this kind of irrational news-managing. So we should give up evidential decision theory, and be causal decision (...)
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  82. Frederick S. Ellett (1984). Bayesian Confirmation and Interpretation. Educational Theory 34 (2):175-182.
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  83. Lefteris Farmakis (2008). Did Tom Kuhn Actually Meet Tom Bayes? Erkenntnis 68 (1):41 - 53.
    Wesley Salmon and John Earman have presented influential Bayesian reconstructions of Thomas Kuhn’s account of theory-change. In this paper I argue that all attempts to give a Bayesian reading of Kuhn’s philosophy of science are fundamentally misguided due to the fact that Bayesian confirmation theory is in fact inconsistent with Kuhn’s account. The reasons for this inconsistency are traced to the role the concept of incommensurability plays with reference to the ‘observational vocabulary’ within Kuhn’s picture of scientific theories. The upshot (...)
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  84. Aidan Feeney, Jonathan Evans & Simon Venn (2008). Rarity, Pseudodiagnosticity and Bayesian Reasoning. Thinking and Reasoning 14 (3):209 – 230.
    Three experiments investigated the effect of rarity on people's selection and interpretation of data in a variant of the pseudodiagnosticity task. For familiar (Experiment 1) but not for arbitrary (Experiment 3) materials, participants were more likely to select evidence so as to complete a likelihood ratio when the initial evidence they received was a single likelihood concerning a rare feature. This rarity effect with familiar materials was replicated in Experiment 2 where it was shown that participants were relatively insensitive to (...)
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  85. Roberto Festa (2003). Induction, Probability, and Bayesian Epistemology. Poznan Studies in the Philosophy of the Sciences and the Humanities 80 (1):251-284.
    Finland is internationally known as one of the leading centers of twentieth century analytic philosophy. This volume offers for the first time an overall survey of the Finnish analytic school. The rise of this trend is illustrated by original articles of Edward Westermarck, Eino Kaila, Georg Henrik von Wright, and Jaakko Hintikka. Contributions of Finnish philosophers are then systematically discussed in the fields of logic, philosophy of language, philosophy of science, history of philosophy, ethics and social philosophy. Metaphilosophical reflections on (...)
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  86. Festa, Roberto, Optimum Inductive Methods. A Study in Inductive Probability, Bayesian Statistics, and Verisimilitude.
    According to the Bayesian view, scientific hypotheses must be appraised in terms of their posterior probabilities relative to the available experimental data. Such posterior probabilities are derived from the prior probabilities of the hypotheses by applying Bayes'theorem. One of the most important problems arising within the Bayesian approach to scientific methodology is the choice of prior probabilities. Here this problem is considered in detail w.r.t. two applications of the Bayesian approach: (1) the theory of inductive probabilities (TIP) developed by Rudolf (...)
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  87. B. Fitelson (2005). Review: Bayesian Epistemology. Mind 114 (454):394-400.
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  88. Branden Fitelson, Bayes's Theorem.
    This is a high quality, concise collection of articles on the foundations of probability and statistics. Its editor, Richard Swinburne, has collected five papers by contemporary leaders in the field, written a pretty thorough and even-handed introductory essay, and placed a very clean and accessible version of Reverend Thomas Bayes’s famous essay (“An Essay Towards the Solving a Problem in the Doctrine of Chances”) at the end, as an Appendix (with a brief historical introduction by the noted statistician G.A. Barnard). (...)
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  89. Branden Fitelson, Some Recent Fallacies of Approximation in Bayesian Confirmation Theory.
    • Several recent Bayesian discussions make use of “approximation” – Earman on the Quantitative Old Evidence Problem – Vranas on Quantitative Approaches to the Ravens Paradox – Dorling’s Quantitative Approach to Duhem–Quine – Strevens’s Quantitative Approach to Duhem–Quine – rThere are also examples not involving confirmation: E.g.
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  90. Branden Fitelson, Earman on Old Evidence and Measures of Confirmation.
    In Bayes or Bust? John Earman quickly dismisses a possible resolution (or avoidance) of the problem of old evidence. In this note, I argue that his dismissal is premature, and that the proposed resolution (when charitably reconstructed) is reasonable.
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  91. Branden Fitelson, How Bayesian Confirmation Theory Handles the Paradox of the Ravens.
    The Paradox of the Ravens (a.k.a,, The Paradox of Confirmation) is indeed an old chestnut. A great many things have been written and said about this paradox and its implications for the logic of evidential support. The first part of this paper will provide a brief survey of the early history of the paradox. This will include the original formulation of the paradox and the early responses of Hempel, Goodman, and Quine. The second part of the paper will describe attempts (...)
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  92. Branden Fitelson (2001). A Bayesian Account of Independent Evidence with Applications. Proceedings of the Philosophy of Science Association 2001 (3):S123-.
    outlined. This account is partly inspired by the work of C.S. Peirce. When we want to consider how degree of confirmation varies with changing I show that a large class of quantitative Bayesian measures of con-.
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  93. Branden Fitelson (2001). Studies in Bayesian Confirmation Theory. Dissertation, University of Wisconsin, Madison
    According to Bayesian confirmation theory, evidence E (incrementally) confirms (or supports) a hypothesis H (roughly) just in case E and H are positively probabilistically correlated (under an appropriate probability function Pr). There are many logically equivalent ways of saying that E and H are correlated under Pr. Surprisingly, this leads to a plethora of non-equivalent quantitative measures of the degree to which E confirms H (under Pr). In fact, many non-equivalent Bayesian measures of the degree to which E confirms (or (...)
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  94. Branden Fitelson (1999). The Plurality of Bayesian Measures of Confirmation and the Problem of Measure Sensitivity. Philosophy of Science 66 (3):378.
    Contemporary Bayesian confirmation theorists measure degree of (incremental) confirmation using a variety of non-equivalent relevance measures. As a result, a great many of the arguments surrounding quantitative Bayesian confirmation theory are implicitly sensitive to choice of measure of confirmation. Such arguments are enthymematic, since they tacitly presuppose that certain relevance measures should be used (for various purposes) rather than other relevance measures that have been proposed and defended in the philosophical literature. I present a survey of this pervasive class of (...)
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  95. Branden Fitelson & James Hawthorne (2010). Wason Task(s) and the Paradox of Confirmation. Philosophical Perspectives 24 (1):207-241.
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  96. Branden Fitelson & James Hawthorne (2010). How Bayesian Confirmation Theory Handles the Paradox of the Ravens. In Ellery Eells & James Fetzer (eds.), The Place of Probability in Science. Springer.
    The Paradox of the Ravens (a.k.a,, The Paradox of Confirmation) is indeed an old chestnut. A great many things have been written and said about this paradox and its implications for the logic of evidential support. The first part of this paper will provide a brief survey of the early history of the paradox. This will include the original formulation of the paradox and the early responses of Hempel, Goodman, and Quine. The second part of the paper will describe attempts (...)
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  97. Branden Fitelson & Andrew Waterman (2007). Comparative Bayesian Confirmation and the Quine-Duhem Problem: A Rejoinder to Strevens. British Journal for the Philosophy of Science 58 (2):332 - 338.
    By and large, we think (Strevens's [2005]) is a useful reply to our original critique (Fitelson and Waterman [2005]) of his article on the Quine-Duhem (QD) problem (Strevens [2001]). But, we remain unsatisfied with several aspects of his reply (and his original article). Ultimately, we do not think he properly addresses our most important worries. In this brief rejoinder, we explain our remaining worries, and we issue a revised challenge for Strevens's approach to QD.
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  98. Branden Fitelson & Andrew Waterman (2005). Bayesian Confirmation and Auxiliary Hypotheses Revisited: A Reply to Strevens. British Journal for the Philosophy of Science 56 (2):293-302.
    has proposed an interesting and novel Bayesian analysis of the Quine-Duhem (Q–D) problem (i.e., the problem of auxiliary hypotheses). Strevens's analysis involves the use of a simplifying idealization concerning the original Q–D problem. We will show that this idealization is far stronger than it might appear. Indeed, we argue that Strevens's idealization oversimplifies the Q–D problem, and we propose a diagnosis of the source(s) of the oversimplification. Some background on Quine–Duhem Strevens's simplifying idealization Indications that (I) oversimplifies Q–D Strevens's argument (...)
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