Results for 'Bayesian'

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  1. Paul Weirich.Bayesian Justification - 1994 - In Dag Prawitz & Dag Westerståhl (eds.), Logic and Philosophy of Science in Uppsala. Kluwer Academic Publishers. pp. 245.
     
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  2. Bayesian Norms and Non-Ideal Agents.Julia Staffel - 2024 - In Maria Lasonen-Aarnio & Clayton Littlejohn (eds.), The Routledge Handbook of the Philosophy of Evidence. New York, NY: Routledge.
    Bayesian epistemology provides a popular and powerful framework for modeling rational norms on credences, including how rational agents should respond to evidence. The framework is built on the assumption that ideally rational agents have credences, or degrees of belief, that are representable by numbers that obey the axioms of probability. From there, further constraints are proposed regarding which credence assignments are rationally permissible, and how rational agents’ credences should change upon learning new evidence. While the details are hotly disputed, (...)
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  3. Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition.Matt Jones & Bradley C. Love - 2011 - Behavioral and Brain Sciences 34 (4):169-188.
    The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. (...)
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  4.  43
    Bayesian Psychiatry and the Social Focus of Delusions.Daniel Williams & Marcella Montagnese - manuscript
    A large and growing body of research in computational psychiatry draws on Bayesian modelling to illuminate the dysfunctions and aberrations that underlie psychiatric disorders. After identifying the chief attractions of this research programme, we argue that its typical focus on abstract, domain-general inferential processes is likely to obscure many of the distinctive ways in which the human mind can break down and malfunction. We illustrate this by appeal to psychosis and the social phenomenology of delusions.
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  5.  86
    Bayesian reverse-engineering considered as a research strategy for cognitive science.Carlos Zednik & Frank Jäkel - 2016 - Synthese 193 (12):3951-3985.
    Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic (...)
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  6.  67
    Bayesian Philosophy of Science.Jan Sprenger & Stephan Hartmann - 2019 - Oxford and New York: Oxford University Press.
    How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms (...)
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  7. Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2003 - Oxford: Oxford University Press. Edited by Stephan Hartmann.
    Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the (...)
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  8. Bayesians Versus Frequentists: A Philosophical Debate on Statistical Reasoning.Jordi Vallverdú - 2016 - Berlin, Heidelberg: Imprint: Springer.
    This book analyzes the origins of statistical thinking as well as its related philosophical questions, such as causality, determinism or chance. Bayesian and frequentist approaches are subjected to a historical, cognitive and epistemological analysis, making it possible to not only compare the two competing theories, but to also find a potential solution. The work pursues a naturalistic approach, proceeding from the existence of numerosity in natural environments to the existence of contemporary formulas and methodologies to heuristic pragmatism, a concept (...)
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  9. The Bayesian Objection.Luca Moretti - 2020 - In Seemings and Epistemic Justification: how appearances justify beliefs. Cham: Springer.
    In this chapter I analyse an objection to phenomenal conservatism to the effect that phenomenal conservatism is unacceptable because it is incompatible with Bayesianism. I consider a few responses to it and dismiss them as misled or problematic. Then, I argue that this objection doesn’t go through because it rests on an implausible formalization of the notion of seeming-based justification. In the final part of the chapter, I investigate how seeming-based justification and justification based on one’s reflective belief that one (...)
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  10.  81
    Bayesian argumentation and the value of logical validity.Benjamin Eva & Stephan Hartmann - 2018 - Psychological Review 125 (5):806-821.
    According to the Bayesian paradigm in the psychology of reasoning, the norms by which everyday human cognition is best evaluated are probabilistic rather than logical in character. Recently, the Bayesian paradigm has been applied to the domain of argumentation, where the fundamental norms are traditionally assumed to be logical. Here, we present a major generalisation of extant Bayesian approaches to argumentation that utilizes a new class of Bayesian learning methods that are better suited to modelling dynamic (...)
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  11. Bayesian Epistemology.Stephan Hartmann & Jan Sprenger - 2010 - In Duncan Pritchard & Sven Bernecker (eds.), The Routledge Companion to Epistemology. London: Routledge. pp. 609-620.
    Bayesian epistemology addresses epistemological problems with the help of the mathematical theory of probability. It turns out that the probability calculus is especially suited to represent degrees of belief (credences) and to deal with questions of belief change, confirmation, evidence, justification, and coherence. Compared to the informal discussions in traditional epistemology, Bayesian epis- temology allows for a more precise and fine-grained analysis which takes the gradual aspects of these central epistemological notions into account. Bayesian epistemology therefore complements (...)
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  12. Bayesian Informal Logic and Fallacy.Kevin Korb - 2004 - Informal Logic 24 (1):41-70.
    Bayesian reasoning has been applied formally to statistical inference, machine learning and analysing scientific method. Here I apply it informally to more common forms of inference, namely natural language arguments. I analyse a variety of traditional fallacies, deductive, inductive and causal, and find more merit in them than is generally acknowledged. Bayesian principles provide a framework for understanding ordinary arguments which is well worth developing.
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  13. The Bayesian and the Dogmatist.Brian Weatherson - 2007 - Proceedings of the Aristotelian Society 107 (1pt2):169-185.
    Dogmatism is sometimes thought to be incompatible with Bayesian models of rational learning. I show that the best model for updating imprecise credences is compatible with dogmatism.
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  14. Bayesian Expressivism.Seth Yalcin - 2012 - Proceedings of the Aristotelian Society 112 (2pt2):123-160.
    I develop a conception of expressivism according to which it is chiefly a pragmatic thesis about some fragment of discourse, one imposing certain constraints on semantics. The first half of the paper uses credal expressivism about the language of probability as a stalking-horse for this purpose. The second half turns to the question of how one might frame an analogous form of expressivism about the language of deontic modality. Here I offer a preliminary comparison of two expressivist lines. The first, (...)
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  15. Bayesian Decision Theory and Stochastic Independence.Philippe Mongin - 2020 - Philosophy of Science 87 (1):152-178.
    As stochastic independence is essential to the mathematical development of probability theory, it seems that any foundational work on probability should be able to account for this property. Bayesian decision theory appears to be wanting in this respect. Savage’s postulates on preferences under uncertainty entail a subjective expected utility representation, and this asserts only the existence and uniqueness of a subjective probability measure, regardless of its properties. What is missing is a preference condition corresponding to stochastic independence. To fill (...)
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  16. Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - British Journal for the Philosophy of Science 68 (2).
    It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon (...)
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  17. Bayesian Nets and Causality: Philosophical and Computational Foundations.Jon Williamson - 2004 - Oxford, England: Oxford University Press.
    Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, brings together two important research topics: how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.
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  18. Bayesian group belief.Franz Dietrich - 2010 - Social Choice and Welfare 35 (4):595-626.
    If a group is modelled as a single Bayesian agent, what should its beliefs be? I propose an axiomatic model that connects group beliefs to beliefs of group members, who are themselves modelled as Bayesian agents, possibly with different priors and different information. Group beliefs are proven to take a simple multiplicative form if people’s information is independent, and a more complex form if information overlaps arbitrarily. This shows that group beliefs can incorporate all information spread over the (...)
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  19.  53
    A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal.Jennifer Culbertson & Paul Smolensky - 2012 - Cognitive Science 36 (8):1468-1498.
    In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word‐order patterns in the nominal domain. The model identifies internal biases (...)
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  20. Bayesian Epistemology.William Talbott - 2006 - Stanford Encyclopedia of Philosophy.
    Bayesian epistemology’ became an epistemological movement in the 20th century, though its two main features can be traced back to the eponymous Reverend Thomas Bayes (c. 1701-61). Those two features are: (1) the introduction of a formal apparatus for inductive logic; (2) the introduction of a pragmatic self-defeat test (as illustrated by Dutch Book Arguments) for epistemic rationality as a way of extending the justification of the laws of deductive logic to include a justification for the laws of inductive (...)
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  21. Bayesians Commit the Gambler's Fallacy.Kevin Dorst - manuscript
    The gambler’s fallacy is the tendency to expect random processes to switch more often than they actually do—for example, to think that after a string of tails, a heads is more likely. It’s often taken to be evidence for irrationality. It isn’t. Rather, it’s to be expected from a group of Bayesians who begin with causal uncertainty, and then observe unbiased data from an (in fact) statistically independent process. Although they converge toward the truth, they do so in an asymmetric (...)
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  22. Bayesian probability.Patrick Maher - 2010 - Synthese 172 (1):119 - 127.
    Bayesian decision theory is here construed as explicating a particular concept of rational choice and Bayesian probability is taken to be the concept of probability used in that theory. Bayesian probability is usually identified with the agent’s degrees of belief but that interpretation makes Bayesian decision theory a poor explication of the relevant concept of rational choice. A satisfactory conception of Bayesian decision theory is obtained by taking Bayesian probability to be an explicatum for (...)
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  23.  60
    Bayesian Cognitive Science, Monopoly, and Neglected Frameworks.Matteo Colombo & Stephan Hartmann - 2015 - British Journal for the Philosophy of Science 68 (2):451–484.
    A widely shared view in the cognitive sciences is that discovering and assessing explanations of cognitive phenomena whose production involves uncertainty should be done in a Bayesian framework. One assumption supporting this modelling choice is that Bayes provides the best approach for representing uncertainty. However, it is unclear that Bayes possesses special epistemic virtues over alternative modelling frameworks, since a systematic comparison has yet to be attempted. Currently, it is then premature to assert that cognitive phenomena involving uncertainty are (...)
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  24. Bayesian Models, Delusional Beliefs, and Epistemic Possibilities.Matthew Parrott - 2016 - British Journal for the Philosophy of Science 67 (1):271-296.
    The Capgras delusion is a condition in which a person believes that an imposter has replaced some close friend or relative. Recent theorists have appealed to Bayesianism to help explain both why a subject with the Capgras delusion adopts this delusional belief and why it persists despite counter-evidence. The Bayesian approach is useful for addressing these questions; however, the main proposal of this essay is that Capgras subjects also have a delusional conception of epistemic possibility, more specifically, they think (...)
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  25. Bayesian Beauty.Silvia Milano - 2020 - Erkenntnis 87 (2):657-676.
    The Sleeping Beauty problem has attracted considerable attention in the literature as a paradigmatic example of how self-locating uncertainty creates problems for the Bayesian principles of Conditionalization and Reflection. Furthermore, it is also thought to raise serious issues for diachronic Dutch Book arguments. I show that, contrary to what is commonly accepted, it is possible to represent the Sleeping Beauty problem within a standard Bayesian framework. Once the problem is correctly represented, the ‘thirder’ solution satisfies standard rationality principles, (...)
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  26. Bayesian Sensorimotor Psychology.Michael Rescorla - 2016 - Mind and Language 31 (1):3-36.
    Sensorimotor psychology studies the mental processes that control goal-directed bodily motion. Recently, sensorimotor psychologists have provided empirically successful Bayesian models of motor control. These models describe how the motor system uses sensory input to select motor commands that promote goals set by high-level cognition. I highlight the impressive explanatory benefits offered by Bayesian models of motor control. I argue that our current best models assign explanatory centrality to a robust notion of mental representation. I deploy my analysis to (...)
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  27. The Bayesian explanation of transmission failure.Geoff Pynn - 2013 - Synthese 190 (9):1519-1531.
    Even if our justified beliefs are closed under known entailment, there may still be instances of transmission failure. Transmission failure occurs when P entails Q, but a subject cannot acquire a justified belief that Q by deducing it from P. Paradigm cases of transmission failure involve inferences from mundane beliefs (e.g., that the wall in front of you is red) to the denials of skeptical hypotheses relative to those beliefs (e.g., that the wall in front of you is not white (...)
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  28. On bayesian measures of evidential support: Theoretical and empirical issues.Vincenzo Crupi, Katya Tentori & and Michel Gonzalez - 2007 - Philosophy of Science 74 (2):229-252.
    Epistemologists and philosophers of science have often attempted to express formally the impact of a piece of evidence on the credibility of a hypothesis. In this paper we will focus on the Bayesian approach to evidential support. We will propose a new formal treatment of the notion of degree of confirmation and we will argue that it overcomes some limitations of the currently available approaches on two grounds: (i) a theoretical analysis of the confirmation relation seen as an extension (...)
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  29.  54
    Bayesian merging of opinions and algorithmic randomness.Francesca Zaffora Blando - forthcoming - British Journal for the Philosophy of Science.
    We study the phenomenon of merging of opinions for computationally limited Bayesian agents from the perspective of algorithmic randomness. When they agree on which data streams are algorithmically random, two Bayesian agents beginning the learning process with different priors may be seen as having compatible beliefs about the global uniformity of nature. This is because the algorithmically random data streams are of necessity globally regular: they are precisely the sequences that satisfy certain important statistical laws. By virtue of (...)
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  30. Bayesian learning models with revision of evidence.William Harper - 1978 - Philosophia 7 (2):357-367.
  31. Bayesian norms and non-ideal agents.Julia Staffel - 2019 - In Maria Lasonen-Aarnio & Clayton Littlejohn (eds.), The Routledge Handbook of the Philosophy of Evidence. Routledge.
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  32. Bayesian Learning Models of Pain: A Call to Action.Abby Tabor & Christopher Burr - 2019 - Current Opinion in Behavioral Sciences 26:54-61.
    Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.
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  33.  44
    A Bayesian Account of Independent Evidence with Applications.Branden Fitelson - 2001 - Philosophy of Science 68 (S3):S123-S140.
    A Bayesian account of independent evidential support is outlined. This account is partly inspired by the work of C. S. Peirce. I show that a large class of quantitative Bayesian measures of confirmation satisfy some basic desiderata suggested by Peirce for adequate accounts of independent evidence. I argue that, by considering further natural constraints on a probabilistic account of independent evidence, all but a very small class of Bayesian measures of confirmation can be ruled out. In closing, (...)
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  34. Improving Bayesian statistics understanding in the age of Big Data with the bayesvl R package.Quan-Hoang Vuong, Viet-Phuong La, Minh-Hoang Nguyen, Manh-Toan Ho, Manh-Tung Ho & Peter Mantello - 2020 - Software Impacts 4 (1):100016.
    The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines (...)
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  35.  72
    Bayesian Rationality: The Probabilistic Approach to Human Reasoning.Mike Oaksford & Nick Chater - 2007 - Oxford University Press.
    Are people rational? This question was central to Greek thought and has been at the heart of psychology and philosophy for millennia. This book provides a radical and controversial reappraisal of conventional wisdom in the psychology of reasoning, proposing that the Western conception of the mind as a logical system is flawed at the very outset. It argues that cognition should be understood in terms of probability theory, the calculus of uncertain reasoning, rather than in terms of logic, the calculus (...)
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  36. Bayesian Decision Theory and Stochastic Independence.Philippe Mongin - 2017 - TARK 2017.
    Stochastic independence has a complex status in probability theory. It is not part of the definition of a probability measure, but it is nonetheless an essential property for the mathematical development of this theory. Bayesian decision theorists such as Savage can be criticized for being silent about stochastic independence. From their current preference axioms, they can derive no more than the definitional properties of a probability measure. In a new framework of twofold uncertainty, we introduce preference axioms that entail (...)
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  37.  1
    Bayesian Teaching Model of image Based on Image Recognition by Deep Learning. 은은숙 - 2020 - Journal of the New Korean Philosophical Association 102:271-296.
    본고는 딥러닝의 이미지 인식 원리와 유아의 이미지 인식 원리를 종합하면서, 이미지-개념 학습을 위한 새로운 교수학습모델, 즉 “베이지안 구조구성주의 교수학습모델”(Bayesian Structure-constructivist Teaching-learning Model: BSTM)을 제안한다. 달리 말하면, 기계학습 원리와 인간학습 원리를 비교함으로써 얻게 되는 시너지 효과를 바탕으로, 유아들의 이미지-개념 학습을 위한 새로운 교수 모델을 구성하는 것을 목표로 한다. 이런 맥락에서 본고는 전체적으로 3가지 차원에서 논의된다. 첫째, 아동의 이미지 학습에 대한 역사적 중요 이론인 “대상 전체론적 가설”, “분류학적 가설”, “배타적 가설”, “기본 수준 범주 가설” 등을 역사 비판적 관점에서 검토한다. 둘째, 컴퓨터 (...)
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  38. A Bayesian Account of the Virtue of Unification.Wayne C. Myrvold - 2003 - Philosophy of Science 70 (2):399-423.
    A Bayesian account of the virtue of unification is given. On this account, the ability of a theory to unify disparate phenomena consists in the ability of the theory to render such phenomena informationally relevant to each other. It is shown that such ability contributes to the evidential support of the theory, and hence that preference for theories that unify the phenomena need not, on a Bayesian account, be built into the prior probabilities of theories.
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  39. Bayesian Networks and the Problem of Unreliable Instruments.Luc Bovens & Stephan Hartmann - 2002 - Philosophy of Science 69 (1):29-72.
    We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments. In particular, we consider (i) repeated measurements of a single test consequence of the hypothesis, (ii) measurements of multiple test consequences of the hypothesis, (iii) theoretical support for the reliability of the instrument, and (iv) calibration procedures. We evaluate these strategies on their relative merits under idealized conditions and show some (...)
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  40.  55
    A Bayesian Account of Psychopathy: A Model of Lacks Remorse and Self-Aggrandizing.Aaron Prosser, Karl Friston, Nathan Bakker & Thomas Parr - 2018 - Computational Psychiatry 2:92-140.
    This article proposes a formal model that integrates cognitive and psychodynamic psychotherapeutic models of psychopathy to show how two major psychopathic traits called lacks remorse and self-aggrandizing can be understood as a form of abnormal Bayesian inference about the self. This model draws on the predictive coding (i.e., active inference) framework, a neurobiologically plausible explanatory framework for message passing in the brain that is formalized in terms of hierarchical Bayesian inference. In summary, this model proposes that these two (...)
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  41. Bayesian Epistemology.Alan Hájek & Stephan Hartmann - 2010 - In DancyJ (ed.), A Companion to Epistemology. Blackwell.
    Bayesianism is our leading theory of uncertainty. Epistemology is defined as the theory of knowledge. So “Bayesian Epistemology” may sound like an oxymoron. Bayesianism, after all, studies the properties and dynamics of degrees of belief, understood to be probabilities. Traditional epistemology, on the other hand, places the singularly non-probabilistic notion of knowledge at centre stage, and to the extent that it traffics in belief, that notion does not come in degrees. So how can there be a Bayesian epistemology?
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  42. Bayesian agnosticism and constructive empiricism.Bradley Monton - 1998 - Analysis 58 (3):207–212.
    This paper addresses the question: how should the traditional doxastic attitude of agnosticism be represented in a Bayesian framework? Bas van Fraassen has one proposal: a Bayesian is agnostic about a proposition if her opinion about the proposition is represented by a probability interval with zero as the lower limit. I argue that van Fraassen's proposal is not adequate. Mark Kaplan claims that this leads to a problem with constructive empiricism; I show that Kaplan's claim is incorrect.
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  43. Bayesian perspectives on mathematical practice.James Franklin - 2020 - Handbook of the History and Philosophy of Mathematical Practice.
    Mathematicians often speak of conjectures as being confirmed by evidence that falls short of proof. For their own conjectures, evidence justifies further work in looking for a proof. Those conjectures of mathematics that have long resisted proof, such as the Riemann hypothesis, have had to be considered in terms of the evidence for and against them. In recent decades, massive increases in computer power have permitted the gathering of huge amounts of numerical evidence, both for conjectures in pure mathematics and (...)
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  44. Bayesian updating when what you learn might be false.Richard Pettigrew - 2023 - Erkenntnis 88 (1):309-324.
    Rescorla (Erkenntnis, 2020) has recently pointed out that the standard arguments for Bayesian Conditionalization assume that whenever I become certain of something, it is true. Most people would reject this assumption. In response, Rescorla offers an improved Dutch Book argument for Bayesian Conditionalization that does not make this assumption. My purpose in this paper is two-fold. First, I want to illuminate Rescorla’s new argument by giving a very general Dutch Book argument that applies to many cases of updating (...)
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  45.  19
    Hyperintensional evidence and Bayesian coherence.Ted Poston - 2024 - Asian Journal of Philosophy 3 (1):1-13.
    Bayesian approaches to rationality require that a person’s degrees of belief be coherent. Among other implications, coherence requires that a person has the same degree of belief in every logically equivalent proposition. However, a person can have evidence for a claim without having evidence for all its propositional equivalences. This paper explores this conflict and argues that a person may be perfectly rational by virtue of responding to their evidence, even if their credences are not coherent. The paper also (...)
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  46.  72
    A Bayesian Account of Reconstructive Memory.Pernille Hemmer & Mark Steyvers - 2009 - Topics in Cognitive Science 1 (1):189-202.
    It is well established that prior knowledge influences reconstruction from memory, but the specific interactions of memory and knowledge are unclear. Extending work by Huttenlocher et al. (Psychological Review, 98 [1991] 352; Journal of Experimental Psychology: General, 129 [2000] 220), we propose a Bayesian model of reconstructive memory in which prior knowledge interacts with episodic memory at multiple levels of abstraction. The combination of prior knowledge and noisy memory representations is dependent on familiarity. We present empirical evidence of the (...)
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  47. Bayesian Orgulity.Gordon Belot - 2013 - Philosophy of Science 80 (4):483-503.
    A piece of folklore enjoys some currency among philosophical Bayesians, according to which Bayesian agents that, intuitively speaking, spread their credence over the entire space of available hypotheses are certain to converge to the truth. The goals of the present discussion are to show that kernel of truth in this folklore is in some ways fairly small and to argue that Bayesian convergence-to-the-truth results are a liability for Bayesianism as an account of rationality, since they render a certain (...)
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  48. Bayesian versus non-Bayesian approaches to confirmation.Colin Howson & Peter Urbach - 2010 - In Antony Eagle (ed.), Philosophy of Probability: Contemporary Readings. New York: Routledge.
  49.  28
    Bayesian Argumentation – The Practical Side of Probability.Frank Zenker (ed.) - 2012 - Springer.
    Relevant to, and drawing from, a range of disciplines, the chapters in this collection show the diversity, and applicability, of research in Bayesian argumentation.
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  50.  61
    Non-Bayesian Inference: Causal Structure Trumps Correlation.Bénédicte Bes, Steven Sloman, Christopher G. Lucas & Éric Raufaste - 2012 - Cognitive Science 36 (7):1178-1203.
    The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of (...)
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