The Experts below are selected from a list of 1992 Experts worldwide ranked by ideXlab platform
Jon Williamson - One of the best experts on this subject based on the ideXlab platform.
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the principal principle and subjective Bayesianism
2020Co-Authors: Christian Wallmann, Jon WilliamsonAbstract: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 some support to this version of objective Bayesianism.
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Justifying Objective Bayesianism on Predicate Languages
2015Co-Authors: Jurgen Landes, Jon WilliamsonAbstract: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 formalism to inductive logic. We show that the maximum entropy principle can be motivated largely in terms of minimising worst-case expected loss.
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objective Bayesianism and the maximum entropy principle
2013Co-Authors: Jurgen Landes, Jon WilliamsonAbstract: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 Bayesianism 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.
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Why Frequentists and Bayesians Need Each Other
2013Co-Authors: Jon WilliamsonAbstract:The orthodox view in statistics has it that frequentism and Bayesianism are diametrically opposed—two totally incompatible takes on the problem of statistical inference. This paper argues to the contrary that the two approaches are complementary and need to mesh if probabilistic reasoning is to be carried out correctly.
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foundations of Bayesianism
2011Co-Authors: David Corfield, Jon WilliamsonAbstract:This is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today. The volume includes important criticisms of Bayesian reasoning and gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. It will be of interest to graduate students, researchers, those involved with the applications of Bayesian reasoning, and philosophers.
Allan Franklin - One of the best experts on this subject based on the ideXlab platform.
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maher mendeleev and Bayesianism
1991Co-Authors: Colin Howson, Allan FranklinAbstract:Maher (1988, 1990) has recently argued that the way a hypothesis is generated can affect its confirmation by the available evidence, and that Bayesian confirmation theory can explain this. In particular, he argues that evidence known at the time a theory was proposed does not confirm the theory as much as it would had that evidence been discovered after the theory was proposed. We examine Maher's arguments for this "predictivist" position and conclude that they do not, in fact, support his view. We also cast doubt on the assumptions of Maher's alleged Bayesian proofs.
Jan Sprenger - One of the best experts on this subject based on the ideXlab platform.
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The objectivity of Subjective Bayesianism
2018Co-Authors: Jan SprengerAbstract:Subjective Bayesianism is a major school of uncertain reasoning and statistical inference. It is often criticized for a lack of objectivity: (i) it opens the door to the influence of values and biases, (ii) evidence judgments can vary substantially between scientists, (iii) 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 (NHST). Second, the criticisms are based on specific senses of objectivity with unclear epistemic value. Third, I show that Subjective Bayesianism promotes other, epistemically relevant senses of scientific objectivity—most notably by increasing the transparency of scientific reasoning.
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the renegade subjectivist jose bernardo s objective Bayesianism
2012Co-Authors: Jan SprengerAbstract:This article motivates and discusses JosE Bernardo's attempt to reconcile the subjective Bayesian framework with a need for objective scientific inference, leading to a special kind of objective Bayesianism, namely reference Bayesianism. We elucidate principal ideas and foundational implications of Bernardo's approach, with particular attention to the classical problem of testing a precise null hypothesis against an unspecified alternative.
Pedro A Ortega - One of the best experts on this subject based on the ideXlab platform.
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subjectivity Bayesianism and causality
2015Co-Authors: Pedro A OrtegaAbstract:"Subjectivity" is studied by comparing Lacanian and Bayesian probability theory.Causality is explained as arising from a two-player game with imperfect information.A measure-theoretic model of the interactive subject is introduced. Display Omitted Bayesian probability theory is one of the most successful frameworks to model reasoning under uncertainty. Its defining property is the interpretation of probabilities as degrees of belief in propositions about the state of the world relative to an inquiring subject. This essay examines the notion of subjectivity by drawing parallels between Lacanian theory and Bayesian probability theory, and concludes that the latter must be enriched with causal interventions to model agency. The central contribution of this work is an abstract model of the subject that accommodates causal interventions in a measure-theoretic formalisation. This formalisation is obtained through a game-theoretic Ansatz based on modelling the inside and outside of the subject as an extensive-form game with imperfect information between two players. Finally, I illustrate the expressiveness of this model with an example of causal induction.
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subjectivity Bayesianism and causality
2014Co-Authors: Pedro A OrtegaAbstract:Bayesian probability theory is one of the most successful frameworks to model reasoning under uncertainty. Its defining property is the interpretation of probabilities as degrees of belief in propositions about the state of the world relative to an inquiring subject. This essay examines the notion of subjectivity by drawing parallels between Lacanian theory and Bayesian probability theory, and concludes that the latter must be enriched with causal interventions to model agency. The central contribution of this work is an abstract model of the subject that accommodates causal interventions in a measure-theoretic formalisation. This formalisation is obtained through a game-theoretic Ansatz based on modelling the inside and outside of the subject as an extensive-form game with imperfect information between two players. Finally, I illustrate the expressiveness of this model with an example of causal induction.
Kenny Easwaran - One of the best experts on this subject based on the ideXlab platform.
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Bayesianism i introduction and arguments in favor
2011Co-Authors: Kenny EaswaranAbstract:Bayesianism is a popular position (or perhaps, positions) in the philosophy of science, epistemology, statistics, and other related areas, which represents belief as coming in degrees, measured by a probability function. In this article, I give an overview of the unifying features of the different positions called ‘Bayesianism’, and discuss several of the arguments traditionally used to support them.
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Bayesianism ii applications and criticisms
2011Co-Authors: Kenny EaswaranAbstract: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, epistemology, and statistics, and criticisms of these applications.