Results for 'Bayesianism'

436 found
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  1.  26
    In Defence of Objective Bayesianism.Jon Williamson - 2010 - Oxford University Press.
    Objective Bayesianism is a methodological theory that is currently applied in statistics, philosophy, artificial intelligence, physics and other sciences. This book develops the formal and philosophical foundations of the theory, at a level accessible to a graduate student with some familiarity with mathematical notation.
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  2. Impermissive Bayesianism.Christopher J. G. Meacham - 2013 - Erkenntnis (S6):1-33.
    This paper examines the debate between permissive and impermissive forms of Bayesianism. It briefly discusses some considerations that might be offered by both sides of the debate, and then replies to some new arguments in favor of impermissivism offered by Roger White. First, it argues that White’s (Oxford studies in epistemology, vol 3. Oxford University Press, Oxford, pp 161–186, 2010) defense of Indifference Principles is unsuccessful. Second, it contends that White’s (Philos Perspect 19:445–459, 2005) arguments against permissive views do (...)
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  3.  50
    Probabilistic Alternatives to Bayesianism: The Case of Explanationism.Igor Douven & Jonah N. Schupbach - 2015 - Frontiers in Psychology 6.
    There has been a probabilistic turn in contemporary cognitive science. Far and away, most of the work in this vein is Bayesian, at least in name. Coinciding with this development, philosophers have increasingly promoted Bayesianism as the best normative account of how humans ought to reason. In this paper, we make a push for exploring the probabilistic terrain outside of Bayesianism. Non-Bayesian, but still probabilistic, theories provide plausible competitors both to descriptive and normative Bayesian accounts. We argue for (...)
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  4.  27
    On Nonparametric Predictive Inference and Objective Bayesianism.F. P. A. Coolen - 2006 - Journal of Logic, Language and Information 15 (1-2):21-47.
    This paper consists of three main parts. First, we give an introduction to Hill’s assumption A (n) and to theory of interval probability, and an overview of recently developed theory and methods for nonparametric predictive inference (NPI), which is based on A (n) and uses interval probability to quantify uncertainty. Thereafter, we illustrate NPI by introducing a variation to the assumption A (n), suitable for inference based on circular data, with applications to several data sets from the literature. This includes (...)
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  5.  58
    Does the Bayesian Solution to the Paradox of Confirmation Really Support Bayesianism?Brian Laetz - 2011 - European Journal for Philosophy of Science 1 (1):39-46.
    Bayesians regard their solution to the paradox of confirmation as grounds for preferring their theory of confirmation to Hempel’s. They point out that, unlike Hempel, they can at least say that a black raven confirms “All ravens are black” more than a white shoe. However, I argue that this alleged advantage is cancelled out by the fact that Bayesians are equally committed to the view that a white shoe confirms “All non-black things are non-ravens” less than a black raven. In (...)
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  6.  26
    Introduction: Bayesianism Into the 21st Century.Jon Williamson & David Corfield - 2001 - In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. pp. 1--16.
    Bayesian theory now incorporates a vast body of mathematical, statistical and computational techniques that are widely applied in a panoply of disciplines, from artificial intelligence to zoology. Yet Bayesians rarely agree on the basics, even on the question of what Bayesianism actually is. This book is about the basics e about the opportunities, questions and problems that face Bayesianism today.
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  7.  4
    Review of Jon Williamson's "In Defense of Objective Bayesianism". [REVIEW]Luis R. G. Oliveira - 2010 - Mathematical Association of America.
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  8. 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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  9. An Objective Justification of Bayesianism II: The Consequences of Minimizing Inaccuracy.Hannes Leitgeb & Richard Pettigrew - 2010 - Philosophy of Science 77 (2):236-272.
    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 prequel, 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 the prequel, we made this norm mathematically precise; in this paper, we derive its consequences. We show that the two core tenets of (...) follow from the norm, while the characteristic claim of the Objectivist Bayesian follows from the norm along with an extra assumption. Finally, we consider Richard Jeffrey’s proposed generalization of conditionalization. We show not only that his rule cannot be derived from the norm, unless the requirement of Rigidity is imposed from the start, but further that the norm reveals it to be illegitimate. We end by deriving an alternative updating rule for those cases in which Jeffrey’s is usually supposed to apply. (shrink)
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  10. Imprecise Bayesianism and Global Belief Inertia.Aron Vallinder - forthcoming - British Journal for the Philosophy of Science:axx033.
    Traditional Bayesianism requires that an agent’s degrees of belief be represented by a real-valued, probabilistic credence function. However, in many cases it seems that our evidence is not rich enough to warrant such precision. In light of this, some have proposed that we instead represent an agent’s degrees of belief as a set of credence functions. This way, we can respect the evidence by requiring that the set, often called the agent’s credal state, includes all credence functions that are (...)
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  11. Likelihoodism, Bayesianism, and Relational Confirmation.Branden Fitelson - 2007 - Synthese 156 (3):473-489.
    Likelihoodists and Bayesians seem to have a fundamental disagreement about the proper probabilistic explication of relational (or contrastive) conceptions of evidential support (or confirmation). In this paper, I will survey some recent arguments and results in this area, with an eye toward pinpointing the nexus of the dispute. This will lead, first, to an important shift in the way the debate has been couched, and, second, to an alternative explication of relational support, which is in some sense a "middle way" (...)
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  12. Bayesianism I: Introduction and Arguments in Favor.Kenny Easwaran - 2011 - Philosophy Compass 6 (5):312-320.
    Bayesianism is a collection of positions in several related fields, centered on the interpretation of probability as something like degree of belief, as contrasted with relative frequency, or objective chance. However, Bayesianism is far from a unified movement. Bayesians are divided about the nature of the probability functions they discuss; about the normative force of this probability function for ordinary and scientific reasoning and decision making; and about what relation (if any) holds between Bayesian and non-Bayesian concepts.
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  13. Objective Bayesianism, Bayesian Conditionalisation and Voluntarism.Jon Williamson - 2011 - Synthese 178 (1):67-85.
    Objective Bayesianism has been criticised on the grounds that objective Bayesian updating, which on a finite outcome space appeals to the maximum entropy principle, differs from Bayesian conditionalisation. The main task of this paper is to show that this objection backfires: the difference between the two forms of updating reflects negatively on Bayesian conditionalisation rather than on objective Bayesian updating. The paper also reviews some existing criticisms and justifications of conditionalisation, arguing in particular that the diachronic Dutch book justification (...)
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  14. 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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  15. Bayesianism and Inference to the Best Explanation.Leah Henderson - 2014 - British Journal for the Philosophy of Science 65 (4):687-715.
    Two of the most influential theories about scientific inference are inference to the best explanation and Bayesianism. How are they related? Bas van Fraassen has claimed that IBE and Bayesianism are incompatible rival theories, as any probabilistic version of IBE would violate Bayesian conditionalization. In response, several authors have defended the view that IBE is compatible with Bayesian updating. They claim that the explanatory considerations in IBE are taken into account by the Bayesian because the Bayesian either does (...)
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  16.  68
    Objective Bayesianism with Predicate Languages.Jon Williamson - 2008 - Synthese 163 (3):341-356.
    Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to first-order logical languages. It is argued that the objective Bayesian should choose a probability function, from all those that satisfy constraints imposed by background knowledge, that is closest to a particular frequency-induced probability function which generalises the λ = 0 function of Carnap’s continuum of inductive methods.
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  17.  7
    Bayesianism.James Joyce - 2004 - In Piers Rawling & Alfred R. Mele (eds.), The Oxford Handbook of Rationality. Oxford: Oxford University Press. pp. 132--155.
    Bayesianism claims to provide a unified theory of epistemic and practical rationality based on the principle of mathematical expectation. In its epistemic guise it requires believers to obey the laws of probability. In its practical guise it asks agents to maximize their subjective expected utility. Joyce’s primary concern is Bayesian epistemology, and its five pillars: people have beliefs and conditional beliefs that come in varying gradations of strength; a person believes a proposition strongly to the extent that she presupposes (...)
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  18. Philosophies of Probability: Objective Bayesianism and its Challenges.Jon Williamson - manuscript
    This chapter presents an overview of the major interpretations of probability followed by an outline of the objective Bayesian interpretation and a discussion of the key challenges it faces. I discuss the ramifications of interpretations of probability and objective Bayesianism for the philosophy of mathematics in general.
     
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  19.  53
    Objective Bayesianism and the Maximum Entropy Principle.Jürgen Landes & Jon Williamson - unknown
    Objective Bayesian epistemology invokes three norms: the strengths of our beliefs should be probabilities, they should be calibrated to our evidence of physical probabilities, and they should otherwise equivocate sufficiently between the basic propositions that we can express. The three norms are sometimes explicated by appealing to the maximum entropy principle, which says that a belief function should be a probability function, from all those that are calibrated to evidence, that has maximum entropy. However, the three norms of objective (...) are usually justified in different ways. In this paper we show that the three norms can all be subsumed under a single justification in terms of minimising worst-case expected loss. This, in turn, is equivalent to maximising a generalised notion of entropy. We suggest that requiring language invariance, in addition to minimising worst-case expected loss, motivates maximisation of standard entropy as opposed to maximisation of other instances of generalised entropy. Our argument also provides a qualified justification for updating degrees of belief by Bayesian conditionalisation. However, conditional probabilities play a less central part in the objective Bayesian account than they do under the subjective view of Bayesianism, leading to a reduced role for Bayes’ Theorem. (shrink)
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  20.  61
    Two Dogmas of Strong Objective Bayesianism.Prasanta S. Bandyopadhyay & Gordon Brittan - 2010 - International Studies in the Philosophy of Science 24 (1):45 – 65.
    We introduce a distinction, unnoticed in the literature, between four varieties of objective Bayesianism. What we call ' strong objective Bayesianism' is characterized by two claims, that all scientific inference is 'logical' and that, given the same background information two agents will ascribe a unique probability to their priors. We think that neither of these claims can be sustained; in this sense, they are 'dogmatic'. The first fails to recognize that some scientific inference, in particular that concerning evidential (...)
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  21. Bayesianism and Reliable Scientific Inquiry.Cory Juhl - 1993 - Philosophy of Science 60 (2):302-319.
    The inductive reliability of Bayesian methods is explored. The first result presented shows that for any solvable inductive problem of a general type, there exists a subjective prior which yields a Bayesian inductive method that solves the problem, although not all subjective priors give rise to a successful inductive method for the problem. The second result shows that the same does not hold for computationally bounded agents, so that Bayesianism is "inductively incomplete" for such agents. Finally a consistency proof (...)
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  22.  64
    Contrastive Bayesianism.Branden Fitelson - 2012 - In Martijn Blaauw (ed.), Contrastivism in Philosophy: New Perspectives. Routledge.
    Bayesianism provides a rich theoretical framework, which lends itself rather naturally to the explication of various “contrastive” and “non-contrastive” concepts. In this (brief) discussion, I will focus on issues involving “contrastivism”, as they arise in some of the recent philosophy of science, epistemology, and cognitive science literature surrounding Bayesian confirmation theory.
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  23.  46
    Bayesianism, Convergence and Social Epistemology.Michael J. Shaffer - 2008 - Episteme 5 (2):pp. 203-219.
    Following the standard practice in sociology, cultural anthropology and history, sociologists, historians of science and some philosophers of science define scientific communities as groups with shared beliefs, values and practices. In this paper it is argued that in real cases the beliefs of the members of such communities often vary significantly in important ways. This has rather dire implications for the convergence defense against the charge of the excessive subjectivity of subjective Bayesianism because that defense requires that communities of (...)
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  24. Objective Bayesianism Defended?Darrell P. Rowbottom - 2012 - Metascience 21 (1):193-196.
    This is a review of Jon Williamson's 'In Defence of Objective Bayesianism'.
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  25.  26
    Der Rabe und der Bayesianist.Mark Siebel - 2004 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 35 (2):313-329.
    The Raven and the Bayesian. As an essential benefit of their probabilistic account of confirmation, Bayesians state that it provides a twofold solution to the ravens paradox. It is supposed to show that (i) the paradox’s conclusion is tenable because a white shoe only negligibly confirms the hypothesis that all ravens are black, and (ii) the paradox’s first premise is false anyway because a black raven can speak against the hypothesis. I argue that both proposals are not only unable to (...)
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  26.  89
    Bayesianism and the Traditional Problem of Induction.Samir Okasha - 2005 - Croatian Journal of Philosophy 5 (2):181-194.
    Many philosophers argue that Bayesian epistemology cannot help us with the traditional Humean problem of induction. I argue that this view is partially but not wholly correct. It is true that Bayesianism does not solve Hume’s problem, in the way that the classical and logical theories of probability aimed to do. However I argue that in one important respect, Hume’s sceptical challenge cannot simply be transposed to a probabilistic context, where beliefs come in degrees, rather than being a yes/no (...)
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  27.  8
    Bayesianism in Mathematics.David Corfield - 2001 - In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. pp. 175--201.
    A study of the possibility of casting plausible matheamtical inference in Bayesian terms.
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  28.  25
    Bayesianism and the Fixity of the Theoretical Framework.Donald Gillies - 2001 - In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. pp. 363--379.
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  29.  11
    Beyond Bayesianism: Comments on Hellman's "Bayes and Beyond".Michael Kruse - 1999 - Philosophy of Science 66 (1):165-174.
    Against Hellman's (1997) recent claims, I argue that Bayesianism is unable to explain the value of generally successful aspects of scientific methodology, viz., deflecting blame from well-confirmed theories onto auxiliaries and preferring more-varied data. Such an explanation would require not just objectification of priors, but a reason to believe priors will generally fall on values that justify the practice. Given the track record on the objectification problem, adding further conditions on priors merely makes the Bayesian's problems even worse.
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  30.  23
    Bayesianism and Causality, or, Why I Am Only a Half-Bayesian.Judea Pearl - 2001 - In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. pp. 19--36.
  31.  44
    Inference to the Best Explanation, Bayesianism, and Feminist Bank Tellers.David Chart - unknown
    Inference to the Best Explanation and Bayesianism have both been proposed as descriptions of the way that people make inferences. This paper argues that one result from cognitive psychology, the "feminist bank teller" experiment, suggests that people use Inference to the Best Explanation rather than Bayesian techniques.
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  32.  54
    Bayesianism and Inference to the Best Explanation.Valeriano Iranzo - 2008 - Theoria 23 (1):89-106.
    Bayesianism and Inference to the best explanation (IBE) are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretationsof prior probabilities: “IBE-Bayesianism” (IBE-Bay) and “frequentist-Bayesianism” (Freq-Bay). After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: (i) endorses a role for explanatory value (...)
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  33.  23
    Justifying Objective Bayesianism on Predicate Languages.Jürgen Landes & Jon Williamson - unknown
    Objective Bayesianism says that the strengths of one’s beliefs ought to be probabilities, calibrated to physical probabilities insofar as one has evidence of them, and otherwise sufficiently equivocal. These norms of belief are often explicated using the maximum entropy principle. In this paper we investigate the extent to which one can provide a unified justification of the objective Bayesian norms in the case in which the background language is a first-order predicate language, with a view to applying the resulting (...)
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  34.  59
    Motivating Objective Bayesianism: From Empirical Constraints to Objective Probabilities.Jon Williamson - manuscript
    Kyburg goes half-way towards objective Bayesianism. He accepts that frequencies constrain rational belief to an interval but stops short of isolating an optimal degree of belief within this interval. I examine the case for going the whole hog.
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  35.  48
    Bayesianism and Language Change.Jon Williamson - 2003 - Journal of Logic, Language and Information 12 (1):53-97.
    Bayesian probability is normally defined over a fixed language or eventspace. But in practice language is susceptible to change, and thequestion naturally arises as to how Bayesian degrees of belief shouldchange as language changes. I argue here that this question poses aserious challenge to Bayesianism. The Bayesian may be able to meet thischallenge however, and I outline a practical method for changing degreesof belief over changes in finite propositional languages.
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  36.  16
    Bayesianism Without the Black Box.Mark Kaplan - 1989 - Philosophy of Science 56 (1):48-69.
    Crucial to bayesian contributions to the philosophy of science has been a characteristic psychology, according to which investigators harbor degree of confidence assignments that (insofar as the agents are rational) obey the axioms of the probability calculus. The rub is that, if the evidence of introspection is to be trusted, this fruitful psychology is false: actual investigators harbor no such assignments. The orthodox bayesian response has been to argue that the evidence of introspection is not to be trusted here; it (...)
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  37.  34
    Bayesianism and Austrian Apriorism.Frank van Dun - unknown
    In the last published round of his debate with Walter Block on economic methodology,1 Bryan Caplan introduces Bayes’ Rule as ‘a cure for methodological schizofrenia’. Block had raised the question ‘Why do economists react so violently to empirical evidence against the conventional view of the minimum wage’s effect?’ and answered it with the suggestion that economists do so because they are covert praxeologists. This means that they base most of their economic arguments on conclusions derived from their a priori understanding (...)
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  38.  12
    'P, and I Have Absolutely No Justification for Believing That P': The Necessary Falsehood of Orthodox Bayesianism.John Williams & Alan Hajek - unknown
    Orthodox Bayesianism tells a story about the epistemic trajectory of an ideally rational agent. The agent begins with a ‘prior’ probability function; thereafter, it conditionalizes on its evidence as it comes in. Consider, then, such an agent at the very beginning of its trajectory. It is ideally rational, but completely ignorant of which world is actual. Call this agent ‘Superbaby’.1 Superbaby personifies the Bayesian story. We argue that it must believe ‘Moorish’ propositions of the form.
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  39.  25
    Bayesianism, —Quo Vadis?—Critical Notice: David Corfield and Jon Williamson (Eds.), Foundations of Bayesianism.Mathias Risse - 2003 - Philosophy of Science 70 (1):225-231.
    This is a review essay about David Corfield and Jon Williamson's anthology Foundations of Bayesianism. Taken together, the fifteen essays assembled in the book assess the state of the art in Bayesianism. Such an assessment is timely, because decision theory and formal epistemology have become disciplines that are no longer taught on a routine basis in good philosophy departments. Thus we need to ask: Quo vadis, Bayesianism? The subjects of the articles include Bayesian group decision theory, approaches (...)
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  40.  19
    Jon Williamson. In Defence of Objective Bayesianism. Oxford: Oxford University Press, 2010. ISBN 978-0-19-922800-3). Pp. Vi + 185. [REVIEW]C. Hennig - 2011 - Philosophia Mathematica 19 (2):219-225.
    The foundations of probability deal with the problem of modelling reasoning in face of uncertainty by a mathematical calculus, usually the standard probability calculus .The three dominating schools in the foundations of probability interpret probabilities as limiting long-run frequencies conceived as an objective property of series of repeatable experiments , or rational betting rates for an individual to bet on the unknown outcome of experiments depending on the individual’s prior assessments updated by evidence , or rational betting rates to bet (...)
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  41.  7
    In Praise of Secular Bayesianism.Evan Heit & Shanna Erickson - 2011 - Behavioral and Brain Sciences 34 (4):202-202.
    It is timely to assess Bayesian models, but Bayesianism is not a religion. Bayesian modeling is typically used as a tool to explain human data. Bayesian models are sometimes equivalent to other models, but have the advantage of explicitly integrating prior hypotheses with new observations. Any lack of representational or neural assumptions may be an advantage rather than a disadvantage.
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  42.  5
    Bayesianism, Convergence and Social Epistemology.Michael Shaffer - 2008 - Episteme 5 (2):203-219.
    Following the standard practice in sociology, cultural anthropology and history, sociologists, historians of science and some philosophers of science define scientific communities as groups with shared beliefs, values and practices. In this paper it is argued that in real cases the beliefs of the members of such communities often vary significantly in important ways. This has rather dire implications for the convergence defense against the charge of the excessive subjectivity of subjective Bayesianism because that defense requires that communities of (...)
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  43.  4
    Bayesianism and Independence.Edward F. Mcclennen - 2001 - In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. pp. 291--307.
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  44.  4
    What is Wrong with Strict Bayesianism?Patrick Maher - 1986 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:450 - 457.
    Bayesian decision theory, in its classical or strict form, requires agents to have a determinate probability function. In recent years many decision theorists have come to think that this requirement should be weakened to allow for cases in which the agent makes indeterminate probability judgments. It has been claimed that this weakening makes the theory more realistic, and that it makes the theory more tenable as a normative ideal. This paper shows that the usual technique for weakening strict Bayesianism (...)
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  45.  1
    Bayesianism as a Set of Meta-Criteria and Its Social Application.Tetsuji Iseda - unknown
    This paper aims at giving a general outlook of Bayesianism as a set of meta-criteria for scientific methodology. In particular, it discusses Social Bayesianism, that is, the application of Bayesian meta-criteria to scientific institutions. From a Bayesian point of view, methodologies and institutions that simulate Bayesian belief updating are good ones, and those with more discriminatory power are better ones than those with less discriminatory power, other things being equal. This paper applies these ideas to a particular issue: (...)
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  46. A Critical Discussion Of The Compatibility Of Bayesianism And Inference To The Best Explanation.Mark Alfano - 2007 - Philosophical Writings 34 (1).
    In this paper I critique Peter Lipton’s attempt to deal with the threat of Bayesianism to the normative aspect of his project in Inference to the Best Explanation. I consider the five approaches Lipton proposes for reconciling the doxastic recommendations of Inference to the Best Explanation with BA’s: IBE gives a ‘boost’ to the posterior probability of particularly ‘lovely’ hypotheses after the Bayesian calculation is performed; IBE helps us to set the likelihood of evidence on a given hypothesis; IBE (...)
     
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  47. Bayesianism and Independence.F. Edward - 2001 - In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. pp. 291.
     
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  48. Bayesianism and the Idea of Scientific Rationality.Jeremiah Joven Joaquin - 2017 - Croatian Journal of Philosophy 17 (1):33-43.
    Bayesianism has been dubbed as the most adequate and successful theory of scientific rationality. Its success mainly lies in its ability to combine two mutually exclusive elements involved in the process of theory-selection in science, viz.: the subjective and objective elements. My aim in this paper is to explain and evaluate Bayesianism’s account of scientific rationality by contrasting it with two other accounts.
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  49. Bayesianism, Infinite Decisions, and Binding.Frank Arntzenius, Adam Elga & John Hawthorne - 2004 - 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 1995). 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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  50.  21
    Bayesianism Versus Baconianism in the Evaluation of Medical Diagnoses.L. Jonathan Cohen - 1980 - British Journal for the Philosophy of Science 31 (1):45-62.
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