Results for 'Jeffrey Conditionalization'

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  1. Jeffrey Conditionalization, the Principal Principle, the Desire as Belief Thesis, and Adams's Thesis.Ittay Nissan-Rozen - 2013 - British Journal for the Philosophy of Science 64 (4):axs039.
    I show that David Lewis’s principal principle is not preserved under Jeffrey conditionalization. Using this observation, I argue that Lewis’s reason for rejecting the desire as belief thesis and Adams’s thesis applies also to his own principal principle. 1 Introduction2 Adams’s Thesis, the Desire as Belief Thesis, and the Principal Principle3 Jeffrey Conditionalization4 The Principal Principles Not Preserved under Jeffrey Conditionalization5 Inadmissible Experiences.
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  2.  45
    Simultaneous Belief Updates Via Successive Jeffrey Conditionalization.Ilho Park - 2013 - Synthese 190 (16):3511-3533.
    This paper discusses simultaneous belief updates. I argue here that modeling such belief updates using the Principle of Minimum Information can be regarded as applying Jeffrey conditionalization successively, and so that, contrary to what many probabilists have thought, the simultaneous belief updates can be successfully modeled by means of Jeffrey conditionalization.
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  3.  25
    Cognitive Penetration 12—16 (See Also 'Objections to Dogmatism and/or Phenomenal Conservatism').Jeffrey Conditionalization - 2013 - In Chris Tucker (ed.), Seemings and Justification: New Essays on Dogmatism and Phenomenal Conservatism. Oup Usa. pp. 2--355.
  4. A Note on Jeffrey Conditionalization.Hartry Field - 1978 - Philosophy of Science 45 (3):361-367.
    Bayesian decision theory can be viewed as the core of psychological theory for idealized agents. To get a complete psychological theory for such agents, you have to supplement it with input and output laws. On a Bayesian theory that employs strict conditionalization, the input laws are easy to give. On a Bayesian theory that employs Jeffrey conditionalization, there appears to be a considerable problem with giving the input laws. However, Jeffrey conditionalization can be reformulated so (...)
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  5.  26
    David Miller. A Paradox of Information. The British Journal for the Philosophy of Science, Vol. 17 No. 1 , Pp. 59–61. - Karl R. Popper. A Comment on Miller's New Paradox of Information. The British Journal for the Philosophy of Science, Vol. 17 No. 1 , Pp. 61–69. - Karl R. Popper. A Paradox of Zero Information. The British Journal for the Philosophy of Science, Vol. 17 No. 2, Pp. 141–143. - J. L. Mackie. Miller's so-Called Paradox of Information.The British Journal for the Philosophy of Science, Vol. 17 No. 2, Pp. 144–147. - David Miller. On a so-Called so-Called Paradox: A Reply to Professor J. L. Mackie.The British Journal for the Philosophy of Science, Vol. 17 No. 2, Pp. 147–149. - Jeffrey Bub and Michael Radner. Miller's Paradox of Information.The British Journal for the Philosophy of Science, Vol. 19 No. 1 , Pp. 63–67. - David Miller. The Straight and Narrow Rule of Induction: A Reply to Dr Bub and Mr Radner.The British Journal for the Philosophy of Science, Vol. 19 No. 2, Pp. 145. [REVIEW]Richard C. Jeffrey - 1970 - Journal of Symbolic Logic 35 (1):124-127.
  6. Probability, Dynamics, and Causality Essays in Honour of Richard C. Jeffrey.Domenico Costantini, Maria Carla Galavotti & Richard C. Jeffrey - 1997
     
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  7.  25
    On the Modal Logic of Jeffrey Conditionalization.Zalán Gyenis - 2018 - Logica Universalis 12 (3-4):351-374.
    We continue the investigations initiated in the recent papers where Bayes logics have been introduced to study the general laws of Bayesian belief revision. In Bayesian belief revision a Bayesian agent revises his prior belief by conditionalizing the prior on some evidence using the Bayes rule. In this paper we take the more general Jeffrey formula as a conditioning device and study the corresponding modal logics that we call Jeffrey logics, focusing mainly on the countable case. The containment (...)
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  8. Sleeping Beauty and Shifted Jeffrey Conditionalization.Namjoong Kim - 2009 - Synthese 168 (2):295-312.
    In this paper, I argue for a view largely favorable to the Thirder view: when Sleeping Beauty wakes up on Monday, her credence in the coin’s landing heads is less than 1/2. Let’s call this “the Lesser view.” For my argument, I (i) criticize Strict Conditionalization as the rule for changing de se credences; (ii) develop a new rule; and (iii) defend it by Gaifman’s Expert Principle. Finally, I defend the Lesser view by making use of this new rule.
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  9.  46
    Order Dependence and Jeffrey Conditionalization.Daniel Osherson - manuscript
    A glance at the sky raises my probability of rain to .7. As it happens, the conditional probabilities of each state given rain remain the same, and similarly for their conditional probabilities given no rain. As Jeffrey (1983, Ch. 11) points out, my new distribution P2 is therefore fixed by the law of total probability. For example, P2(RC) = P2(RC | R)P2(R)+P2(RC | ¯.
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  10. Is Jeffrey Conditionalization Defective By Virtue of Being Non-Commutative? Remarks on the Sameness of Sensory Experiences.Marc Lange - 2000 - Synthese 123 (3):393-403.
  11.  43
    Field and Jeffrey Conditionalization.Daniel Garber - 1980 - Philosophy of Science 47 (1):142-145.
  12.  34
    A Demonstration of the Jeffrey Conditionalization Rule.Bas C. Fraassen - 1986 - Erkenntnis 24 (1):17 - 24.
  13.  9
    A Demonstration of the Jeffrey Conditionalization Rule.Bas C. van Fraassen - 1986 - Erkenntnis 24 (1):17-24.
  14. Understanding Conditionalization.Christopher J. G. Meacham - 2015 - Canadian Journal of Philosophy 45 (5):767-797.
    At the heart of the Bayesianism is a rule, Conditionalization, which tells us how to update our beliefs. Typical formulations of this rule are underspecified. This paper considers how, exactly, this rule should be formulated. It focuses on three issues: when a subject’s evidence is received, whether the rule prescribes sequential or interval updates, and whether the rule is narrow or wide scope. After examining these issues, it argues that there are two distinct and equally viable versions of (...) to choose from. And which version we choose has interesting ramifications, bearing on issues such as whether Conditionalization can handle continuous evidence, and whether Jeffrey Conditionalization is really a generalization of Conditionalization. (shrink)
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  15.  17
    Confirmation Based on Analogical Inference: Bayes Meets Jeffrey.Christian J. Feldbacher-Escamilla & Alexander Gebharter - forthcoming - Canadian Journal of Philosophy.
    Certain hypotheses cannot be directly confirmed for theoretical, practical, or moral reasons. For some of these hypotheses, however, there might be a workaround: confirmation based on analogical reasoning. In this paper we take up Dardashti, Hartmann, Thébault, and Winsberg’s (in press) idea of analyzing confirmation based on analogical inference Baysian style. We identify three types of confirmation by analogy and show that Dardashti et al.’s approach can cover two of them. We then highlight possible problems with their model as a (...)
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  16.  63
    Leitgeb and Pettigrew on Accuracy and Updating.Benjamin Anders Levinstein - 2012 - Philosophy of Science 79 (3):413-424.
    Leitgeb and Pettigrew argue that (1) agents should minimize the expected inaccuracy of their beliefs and (2) inaccuracy should be measured via the Brier score. They show that in certain diachronic cases, these claims require an alternative to Jeffrey Conditionalization. I claim that this alternative is an irrational updating procedure and that the Brier score, and quadratic scoring rules generally, should be rejected as legitimate measures of inaccuracy.
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  17. Diachronic Dutch Books and Evidential Import.J. Dmitri Gallow - 2019 - Philosophy and Phenomenological Research 99 (1):49-80.
    A handful of well-known arguments (the 'diachronic Dutch book arguments') rely upon theorems establishing that, in certain circumstances, you are immune from sure monetary loss (you are not 'diachronically Dutch bookable') if and only if you adopt the strategy of conditionalizing (or Jeffrey conditionalizing) on whatever evidence you happen to receive. These theorems require non-trivial assumptions about which evidence you might acquire---in the case of conditionalization, the assumption is that, if you might learn that e, then it is (...)
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  18.  62
    For True Conditionalizers Weisberg’s Paradox is a False Alarm.Franz Huber - 2014 - Symposion: Theoretical and Applied Inquiries in Philosophy and Social Sciences 1 (1):111-119.
    Weisberg introduces a phenomenon he terms perceptual undermining. He argues that it poses a problem for Jeffrey conditionalization, and Bayesian epistemology in general. This is Weisberg’s paradox. Weisberg argues that perceptual undermining also poses a problem for ranking theory and for Dempster-Shafer theory. In this note I argue that perceptual undermining does not pose a problem for any of these theories: for true conditionalizers Weisberg’s paradox is a false alarm.
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  19.  43
    Confirmation Measures and Collaborative Belief Updating.Ilho Park - 2014 - Synthese 191 (16):3955-3975.
    There are some candidates that have been thought to measure the degree to which evidence incrementally confirms a hypothesis. This paper provides an argument for one candidate—the log-likelihood ratio measure. For this purpose, I will suggest a plausible requirement that I call the Requirement of Collaboration. And then, it will be shown that, of various candidates, only the log-likelihood ratio measure \(l\) satisfies this requirement. Using this result, Jeffrey conditionalization will be reformulated so as to disclose explicitly what (...)
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  20.  52
    Rescuing Reflection.Ilho Park - 2012 - Philosophy of Science 79 (4):473-489.
    In this article, I suggest an argument that seems to show a conflict between the reflection principle and conditionalization. In particular, I show that when the reflection principle is formulated in a standard way, the principle conflicts with Jeffrey conditionalization. And it is argued that the source of the conflict resides in an ambiguity of the standard formulation. Furthermore, I attempt to rescue the principle using Bayes factors. That is, I suggest a new formulation of the principle (...)
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  21.  89
    Holistic Conditionalization and Underminable Perceptual Learning.Brian T. Miller - forthcoming - Philosophy and Phenomenological Research.
    Seeing a red hat can (i) increase my credence in the hat is red, and (ii) introduce a negative dependence between that proposition and po- tential undermining defeaters such as the light is red. The rigidity of Jeffrey Conditionalization makes this awkward, as rigidity preserves inde- pendence. The picture is less awkward given ‘Holistic Conditionalization’, or so it is claimed. I defend Jeffrey Conditionalization’s consistency with underminable perceptual learning and its superiority to Holistic Conditionalization, (...)
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  22. Another Approach to Consensus and Maximally Informed Opinions with Increasing Evidence.Rush T. Stewart & Michael Nielsen - 2018 - Philosophy of Science (2):236-254.
    Merging of opinions results underwrite Bayesian rejoinders to complaints about the subjective nature of personal probability. Such results establish that sufficiently similar priors achieve consensus in the long run when fed the same increasing stream of evidence. Initial subjectivity, the line goes, is of mere transient significance, giving way to intersubjective agreement eventually. Here, we establish a merging result for sets of probability measures that are updated by Jeffrey conditioning. This generalizes a number of different merging results in the (...)
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  23.  10
    Probability, Coherent Belief and Coherent Belief Changes.John Cantwell & Hans Rott - forthcoming - Annals of Mathematics and Artificial Intelligence.
    This paper is about the statics and dynamics of belief states that are represented by pairs consisting of an agent's credences (represented by a subjective probability measure) and her categorical beliefs (represented by a set of possible worlds). Regarding the static side, we argue that the latter proposition should be coherent with respect to the probability measure and that its probability should reach a certain threshold value. On the dynamic side, we advocate Jeffrey conditionalisation as the principal mode of (...)
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  24. Is There a Dutch Book Argument for Probability Kinematics?Brad Armendt - 1980 - Philosophy of Science 47 (4):583-588.
    Dutch Book arguments have been presented for static belief systems and for belief change by conditionalization. An argument is given here that a rule for belief change which under certain conditions violates probability kinematics will leave the agent open to a Dutch Book.
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  25. Three Models of Sequential Belief Updating on Uncertain Evidence.James Hawthorne - 2004 - Journal of Philosophical Logic 33 (1):89-123.
    Jeffrey updating is a natural extension of Bayesian updating to cases where the evidence is uncertain. But, the resulting degrees of belief appear to be sensitive to the order in which the uncertain evidence is acquired, a rather un-Bayesian looking effect. This order dependence results from the way in which basic Jeffrey updating is usually extended to sequences of updates. The usual extension seems very natural, but there are other plausible ways to extend Bayesian updating that maintain order-independence. (...)
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  26.  44
    A Dilemma for the Imprecise Bayesian.Namjoong Kim - 2016 - Synthese 193 (6):1681-1702.
    Many philosophers regard the imprecise credence framework as a more realistic model of probabilistic inferences with imperfect empirical information than the traditional precise credence framework. Hence, it is surprising that the literature lacks any discussion on how to update one’s imprecise credences when the given evidence itself is imprecise. To fill this gap, I consider two updating principles. Unfortunately, each of them faces a serious problem. The first updating principle, which I call “generalized conditionalization,” sometimes forces an agent to (...)
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  27. Probability and the Art of Judgment.Richard Jeffrey - 1992 - Cambridge University Press.
    Richard Jeffrey is beyond dispute one of the most distinguished and influential philosophers working in the field of decision theory and the theory of knowledge. His work is distinctive in showing the interplay of epistemological concerns with probability and utility theory. Not only has he made use of standard probabilistic and decision theoretic tools to clarify concepts of evidential support and informed choice, he has also proposed significant modifications of the standard Bayesian position in order that it provide a (...)
     
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  28. Updating, Undermining, and Independence.Jonathan Weisberg - 2015 - British Journal for the Philosophy of Science 66 (1):121-159.
    Sometimes appearances provide epistemic support that gets undercut later. In an earlier paper I argued that standard Bayesian update rules are at odds with this phenomenon because they are ‘rigid’. Here I generalize and bolster that argument. I first show that the update rules of Dempster–Shafer theory and ranking theory are rigid too, hence also at odds with the defeasibility of appearances. I then rebut three Bayesian attempts to solve the problem. I conclude that defeasible appearances pose a more difficult (...)
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  29. A Probabilistic Analysis of Argument Cogency.David Godden & Frank Zenker - 2018 - Synthese 195 (4):1715-1740.
    This paper offers a probabilistic treatment of the conditions for argument cogency as endorsed in informal logic: acceptability, relevance, and sufficiency. Treating a natural language argument as a reason-claim-complex, our analysis identifies content features of defeasible argument on which the RSA conditions depend, namely: change in the commitment to the reason, the reason’s sensitivity and selectivity to the claim, one’s prior commitment to the claim, and the contextually determined thresholds of acceptability for reasons and for claims. Results contrast with, and (...)
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  30. Regression to the Mean and Judy Benjamin.Randall G. McCutcheon - 2018 - Synthese:1-13.
    Van Fraassen's Judy Benjamin problem asks how one ought to update one's credence in A upon receiving evidence of the sort ``A may or may not obtain, but B is k times likelier than C'', where {A,B,C} is a partition. Van Fraassen's solution, in the limiting case of increasing k, recommends a posterior converging to the probability of A conditional on A union B, where P is one's prior probability function. Grove and Halpern, and more recently Douven and Romeijn, have (...)
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  31.  56
    A Causal Understanding of When and When Not to Jeffrey Conditionalize.Ben Schwan & Reuben Stern - 2017 - Philosophers' Imprint 17.
    There are cases of ineffable learning — i. e., cases where an agent learns something, but becomes certain of nothing that she can express — where it is rational to update by Jeffrey conditionalization. But there are likewise cases of ineffable learning where updating by Jeffrey conditionalization is irrational. In this paper, we first characterize a novel class of cases where it is irrational to update by Jeffrey conditionalization. Then we use the d-separation criterion (...)
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  32. Formal Logic: Its Scope and Limits.Richard C. Jeffrey - 1967 - Hackett.
    This brief paperback is designed for symbolic/formal logic courses. It features the tree method proof system developed by Jeffrey. The new edition contains many more examples and exercises and is reorganized for greater accessibility.
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  33.  17
    Credence for Conclusions: A Brief for Jeffrey’s Rule.John R. Welch - forthcoming - Synthese:1-22.
    Some arguments are good; others are not. How can we tell the difference? This article advances three proposals as a partial answer to this question. The proposals are keyed to arguments conditioned by different degrees of uncertainty: mild, where the argument’s premises are hedged with point-valued probabilities; moderate, where the premises are hedged with interval probabilities; and severe, where the premises are hedged with non-numeric plausibilities such as ‘very likely’ or ‘unconfirmed’. For mild uncertainty, the article proposes to apply a (...)
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  34.  93
    Reasoning with Comparative Moral Judgements: An Argument for Moral Bayesianism.Ittay Nissan-Rozen - 2017 - In Rafal Urbaniak & Gillman Payette (eds.), Applications of Formal Philosophy. The Road Less Travelled. Cham: Springer. pp. 113-136.
    The paper discusses the notion of reasoning with comparative moral judgements (i.e judgements of the form “act a is morally superior to act b”) from the point of view of several meta-ethical positions. Using a simple formal result, it is argued that only a version of moral cognitivism that is committed to the claim that moral beliefs come in degrees can give a normatively plausible account of such reasoning. Some implications of accepting such a version of moral cognitivism are discussed.
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  35. Philosophy of Probability: Contemporary Readings.Antony Eagle (ed.) - 2010 - Routledge.
    _Philosophy of Probability: Contemporary Readings_ is the first anthology to collect essential readings in this important area of philosophy. Featuring the work of leading philosophers in the field such as Carnap, Hájek, Jeffrey, Joyce, Lewis, Loewer, Popper, Ramsey, van Fraassen, von Mises, and many others, the book looks in depth at the following key topics: subjective probability and credence probability updating: conditionalization and reflection Bayesian confirmation theory classical, logical, and evidential probability frequentism physical probability: propensities and objective chances. (...)
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  36.  56
    Von Neumann's Projection Postulate as a Probability Conditionalization Rule in Quantum Mechanics.Jeffrey Bub - 1977 - Journal of Philosophical Logic 6 (1):381 - 390.
  37. An Objective Justification of Bayesianism I: Measuring Inaccuracy.Hannes Leitgeb & Richard Pettigrew - 2010 - Philosophy of Science 77 (2):201-235.
    One of the fundamental problems of epistemology is to say when the evidence in an agent’s possession justifies the beliefs she holds. In this paper and its sequel, we defend the Bayesian solution to this problem by appealing to the following fundamental norm: Accuracy An epistemic agent ought to minimize the inaccuracy of her partial beliefs. In this paper, we make this norm mathematically precise in various ways. We describe three epistemic dilemmas that an agent might face if she attempts (...)
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  38. Commutativity or Holism? A Dilemma for Conditionalizers.Jonathan Weisberg - 2009 - British Journal for the Philosophy of Science 60 (4):793-812.
    Conditionalization and Jeffrey Conditionalization cannot simultaneously satisfy two widely held desiderata on rules for empirical learning. The first desideratum is confirmational holism, which says that the evidential import of an experience is always sensitive to our background assumptions. The second desideratum is commutativity, which says that the order in which one acquires evidence shouldn't affect what conclusions one draws, provided the same total evidence is gathered in the end. (Jeffrey) Conditionalization cannot satisfy either of these (...)
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  39. Bayesianism II: Applications and Criticisms.Kenny Easwaran - 2011 - Philosophy Compass 6 (5):321-332.
    In the first paper, I discussed the basic claims of Bayesianism (that degrees of belief are important, that they obey the axioms of probability theory, and that they are rationally updated by either standard or Jeffrey conditionalization) and the arguments that are often used to support them. In this paper, I will discuss some applications these ideas have had in confirmation theory, epistemol- ogy, and statistics, and criticisms of these applications.
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  40. How to Learn From Theory-Dependent Evidence; or Commutativity and Holism: A Solution for Conditionalizers.J. Dmitri Gallow - 2014 - British Journal for the Philosophy of Science 65 (3):493-519.
    Weisberg ([2009]) provides an argument that neither conditionalization nor Jeffrey conditionalization is capable of accommodating the holist’s claim that beliefs acquired directly from experience can suffer undercutting defeat. I diagnose this failure as stemming from the fact that neither conditionalization nor Jeffrey conditionalization give any advice about how to rationally respond to theory-dependent evidence, and I propose a novel updating procedure that does tell us how to respond to evidence like this. This holistic updating (...)
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  41.  36
    General Properties of Bayesian Learning as Statistical Inference Determined by Conditional Expectations.Zalán Gyenis & Miklós Rédei - 2017 - Review of Symbolic Logic 10 (4):719-755.
    We investigate the general properties of general Bayesian learning, where “general Bayesian learning” means inferring a state from another that is regarded as evidence, and where the inference is conditionalizing the evidence using the conditional expectation determined by a reference probability measure representing the background subjective degrees of belief of a Bayesian Agent performing the inference. States are linear functionals that encode probability measures by assigning expectation values to random variables via integrating them with respect to the probability measure. If (...)
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  42. Preference Among Preferences.Richard C. Jeffrey - 1974 - Journal of Philosophy 71 (13):377-391.
  43. Confirmational Holism and Bayesian Epistemology.David Christensen - 1992 - 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 (...)
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  44.  48
    Why Bayesian Psychology is Incomplete.Frank Döring - 1999 - 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 (...)
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  45.  37
    General Properties of General Bayesian Learning.Miklós Rédei & Zalán Gyenis - unknown
    We investigate the general properties of general Bayesian learning, where ``general Bayesian learning'' means inferring a state from another that is regarded as evidence, and where the inference is conditionalizing the evidence using the conditional expectation determined by a reference probability measure representing the background subjective degrees of belief of a Bayesian Agent performing the inference. States are linear functionals that encode probability measures by assigning expectation values to random variables via integrating them with respect to the probability measure. If (...)
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  46.  42
    Why Bayesian Psychology Is Incomplete.Frank Doring - 1999 - Philosophy of Science 66 (S1):S379 - S389.
    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 (...)
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  47. Logicism Lite.Richard Jeffrey - 2002 - Philosophy of Science 69 (3):474-496.
    Logicism Lite counts number‐theoretical laws as logical for the same sort of reason for which physical laws are counted as as empirical: because of the character of the data they are responsible to. In the case of number theory these are the data verifying or falsifying the simplest equations, which Logicism Lite counts as true or false depending on the logical validity or invalidity of first‐order argument forms in which no numbertheoretical notation appears.
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  48. Reply to Crupi Et Al.'s ‘Confirmation by Uncertain Evidence’.Franz Huber - 2008 - British Journal for the Philosophy of Science 59 (2):213-215.
    Crupi et al. propose a generalization of Bayesian confirmation theory that they claim to adequately deal with confirmation by uncertain evidence. Consider a series of points of time t0, . . . , ti, . . . , tn such that the agent’s subjective probability for an atomic proposition E changes from Pr0 at t0 to . . . to Pri at ti to . . . to Prn at tn. It is understood that the agent’s subjective probabilities change for (...)
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  49.  59
    Indefinite Probability Judgment: A Reply to Levi.Richard Jeffrey - 1987 - Philosophy of Science 54 (4):586-591.
    Isaac Levi and I have different views of probability and decision making. Here, without addressing the merits, I will try to answer some questions recently asked by Levi (1985) about what my view is, and how it relates to his.
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  50.  74
    Desire-as-Belief Implies Opinionation or Indifference.Horacio Costa, John Collins & Isaac Levi - 1995 - Analysis 55 (1):2-5.
    The anti- Humean proposal of constructing desire as belief about what would be good must be abandoned on pain of triviality. Our central result shows that if an agent's belief- desire state is represented by Jeffrey's expected value theory enriched with the Desire as Belief Thesis (DAB), then, provided that three pairwise inconsistent propositions receive non- zero probability, the agent must view with indifference any proposition whose probability is greater than zero. Unlike previous results against DAB our Opinionation or (...)
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