Results for 'Bayesianism'

497 found
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  1. Impermissive Bayesianism.Christopher Meacham - 2013 - Erkenntnis 79 (Suppl 6):1185-1217.
    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 defense of Indifference Principles is unsuccessful. Second, it contends that White’s arguments against permissive views do not succeed.
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  2.  54
    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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  3. Troubles with Bayesianism: An Introduction to the Psychological Immune System.Eric Mandelbaum - 2019 - Mind and Language 34 (2):141-157.
    A Bayesian mind is, at its core, a rational mind. Bayesianism is thus well-suited to predict and explain mental processes that best exemplify our ability to be rational. However, evidence from belief acquisition and change appears to show that we do not acquire and update information in a Bayesian way. Instead, the principles of belief acquisition and updating seem grounded in maintaining a psychological immune system rather than in approximating a Bayesian processor.
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  4. Bayesianism for Non-Ideal Agents.Mattias Skipper & Jens Christian Bjerring - forthcoming - Erkenntnis:1-23.
    Orthodox Bayesianism is a highly idealized theory of how we ought to live our epistemic lives. One of the most widely discussed idealizations is that of logical omniscience: the assumption that an agent’s degrees of belief must be probabilistically coherent to be rational. It is widely agreed that this assumption is problematic if we want to reason about bounded rationality, logical learning, or other aspects of non-ideal epistemic agency. Yet, we still lack a satisfying way to avoid logical omniscience (...)
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  5. 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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  6.  95
    New Theory About Old Evidence. A Framework for Open-Minded Bayesianism.Sylvia9 Wenmackers & Jan-Willem Romeijn - 2016 - Synthese 193 (4).
    We present a conservative extension of a Bayesian account of confirmation that can deal with the problem of old evidence and new theories. So-called open-minded Bayesianism challenges the assumption—implicit in standard Bayesianism—that the correct empirical hypothesis is among the ones currently under consideration. It requires the inclusion of a catch-all hypothesis, which is characterized by means of sets of probability assignments. Upon the introduction of a new theory, the former catch-all is decomposed into a new empirical hypothesis and (...)
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  7.  45
    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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  8.  75
    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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  9.  21
    Review of Jon Williamson's "In Defense of Objective Bayesianism". [REVIEW]Luis R. G. Oliveira - 2010 - Mathematical Association of America.
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  10.  41
    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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  11. 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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  12. 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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  13. Imprecise Bayesianism and Global Belief Inertia.Aron Vallinder - 2018 - British Journal for the Philosophy of Science 69 (4):1205-1230.
    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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  14. 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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  15. 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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  16. 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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  17. 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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  18. 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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  19.  8
    The Principal Principle and subjective Bayesianism.Christian Wallmann & Jon Williamson - 2020 - European Journal for Philosophy of Science 10 (1):1-14.
    This paper poses a problem for Lewis’ Principal Principle in a subjective Bayesian framework: we show that, where chances inform degrees of belief, subjective Bayesianism fails to validate normal informal standards of what is reasonable. This problem points to a tension between the Principal Principle and the claim that conditional degrees of belief are conditional probabilities. However, one version of objective Bayesianism has a straightforward resolution to this problem, because it avoids this latter claim. The problem, then, offers (...)
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  20.  89
    The Objectivity of Subjective Bayesianism.Jan Sprenger - 2018 - European Journal for Philosophy of Science 8 (3):539-558.
    Subjective Bayesianism is a major school of uncertain reasoning and statistical inference. It is often criticized for a lack of objectivity: it opens the door to the influence of values and biases, evidence judgments can vary substantially between scientists, it is not suited for informing policy decisions. My paper rebuts these concerns by connecting the debates on scientific objectivity and statistical method. First, I show that the above concerns arise equally for standard frequentist inference with null hypothesis significance tests. (...)
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  21.  49
    Bayesianism.James M. 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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  22.  71
    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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  23.  71
    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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  24. Book Review : Objective Bayesianism Defended? [REVIEW]Darrell Patrick Rowbottom - 2012 - Metascience 21 (1):193-196.
    Darrell P. Rowbottom reviews the book "In defense of objective Bayesianism" by Jon Williamson.
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  25.  36
    Open-Minded Orthodox Bayesianism by Epsilon-Conditionalization.Eric Raidl - 2020 - British Journal for the Philosophy of Science 71 (1):139-176.
    Orthodox Bayesianism endorses revising by conditionalization. This paper investigates the zero-raising problem, or equivalently the certainty-dropping problem of orthodox Bayesianism: previously neglected possibilities remain neglected, although the new evidence might suggest otherwise. Yet, one may want to model open-minded agents, that is, agents capable of raising previously neglected possibilities. Different reasons can be given for open-mindedness, one of which is fallibilism. The paper proposes a family of open-minded propositional revisions depending on a parameter ϵ. The basic idea is (...)
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  26. 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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  27. Bayesianism and Inference to the Best Explanation.Valeriano Iranzo - 2008 - Theoria : An International Journal for Theory, History and Fundations of Science 23 (1):89-106.
    Bayesianism and Inference to the best explanation 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 interpretations of prior probabilities: “IBE-Bayesianism” and “frequentist-Bayesianism”. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: endorses a role for explanatory value in the assessment (...)
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  28.  21
    Quantum Bayesianism Assessed.John Earman - unknown - The Monist 102 (4):403-423.
    The idea that the quantum probabilities are best construed as the personal/subjective degrees of belief of Bayesian agents is an old one. In recent years the idea has been vigorously pursued by a group of physicists who fly the banner of quantum Bayesianism. The present paper aims to identify the prospects and problems of implementing QBism, and it critically assesses the claim that QBism provides a resolution of some of the long-standing foundations issues in quantum mechanics, including the measurement (...)
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  29.  11
    J. Williamson, In Defence of Objective Bayesianism. Oxford, IN: Oxford University Press Inc., New York, 2010. Iv + 183 Pp. ISBN 978-0-19-922800-3.George Masterton - forthcoming - Cogency - Journal of Reasoning and Argumentation.
    Book review of Jon Williamson's `In Defence of objective Bayesianism'.
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  30.  79
    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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  31. 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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  32. 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.
  33.  3
    Enculturation Without TTOM and Bayesianism Without FEP: Another Bayesian Theory of Culture is Needed.Martin Fortier-Davy - 2020 - Behavioral and Brain Sciences 43.
    First, I discuss cross-cultural evidence showing that a good deal of enculturation takes place outside of thinking through other minds. Second, I review evidence challenging the claim that humans seek to minimize entropy. Finally, I argue that optimality claims should be avoided, and that descriptive Bayesianism offers a more promising avenue for the development of a Bayesian theory of culture.
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  34.  22
    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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  35. 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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  36.  74
    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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  37.  87
    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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  38.  33
    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.
  39.  41
    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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  40.  49
    Bayesianism, Quo Vadis? —Critical Notice: David Corfield and Jon Williamson , Foundations of BayesianismDavid Corfield and Jon Williamson , Foundations of Bayesianism. Dordrecht: Kluwer Academic Publishers , 428 Pp. $110.00. [REVIEW]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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  41. 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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  42.  73
    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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  43.  35
    Higher-Order Beliefs and the Undermining Problem for Bayesianism.Lisa Cassell - 2019 - Acta Analytica 34 (2):197-213.
    Jonathan Weisberg has argued that Bayesianism’s rigid updating rules make Bayesian updating incompatible with undermining defeat. In this paper, I argue that when we attend to the higher-order beliefs we must ascribe to agents in the kinds of cases Weisberg considers, the problem he raises disappears. Once we acknowledge the importance of higher-order beliefs to the undermining story, we are led to a different understanding of how these cases arise. And on this different understanding of things, the rigid nature (...)
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  44.  56
    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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  45.  23
    Inductive Influence: Objective Bayesianism has Been Criticised for Not Allowing Learning From Experience: It is Claimed That an Agent Must Give Degree of Belief Formula to the Next Raven Being Black, However Many Other Black Ravens Have Been Observed. I Argue That This Objection Can Be Overcome by Appealing to Objective Bayesian Nets, a Formalism for Representing Objective Bayesian Degrees of Belief. Under This Account, Previous Observations Exert an Inductive Influence on the Next Observation. I Show How This Approach Can Be Used to Capture the Johnson–Carnap Continuum of Inductive Methods, as Well as the Nix–Paris Continuum, and Show How Inductive Influence Can Be Measured. [REVIEW]Jon Williamson - 2007 - British Journal for the Philosophy of Science 58 (4):689-708.
    Objective Bayesianism has been criticised for not allowing learning from experience: it is claimed that an agent must give degree of belief 12 to the next raven being black, however many other black ravens have been observed. I argue that this objection can be overcome by appealing to objective Bayesian nets, a formalism for representing objective Bayesian degrees of belief. Under this account, previous observations exert an inductive influence on the next observation. I show how this approach can be (...)
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  46.  4
    The Principal Principle and subjective Bayesianism.Christian Wallmann & Jon Williamson - 2020 - European Journal for Philosophy of Science 10 (1):1-14.
    This paper poses a problem for Lewis’ Principal Principle in a subjective Bayesian framework: we show that, where chances inform degrees of belief, subjective Bayesianism fails to validate normal informal standards of what is reasonable. This problem points to a tension between the Principal Principle and the claim that conditional degrees of belief are conditional probabilities. However, one version of objective Bayesianism has a straightforward resolution to this problem, because it avoids this latter claim. The problem, then, offers (...)
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  47.  39
    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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  48.  37
    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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  49. 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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  50.  30
    'P, and I Have Absolutely No Justification for Believing That P': The Necessary Falsehood of Orthodox Bayesianism.John N. 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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