Bayesian Epistemology

Oxford: Oxford University Press. Edited by Stephan Hartmann (2003)
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Abstract

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 information sources. Measurement of the coherence of information is a controversial matter: arguably, the more coherent a set of information is, the more confident we may be that its content is true, other things being equal. The authors offer a new treatment of coherence which respects this claim and shows its relevance to scientific theory choice. Bovens and Hartmann apply this methodology to a wide range of much discussed issues regarding evidence, testimony, scientific theories, and voting. Bayesian Epistemology is an essential tool for anyone working on probabilistic methods in philosophy, and has broad implications for many other disciplines.

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Author Profiles

Luc Bovens
University of North Carolina, Chapel Hill
Stephan Hartmann
Ludwig Maximilians Universität, München

Citations of this work

Bayesian Philosophy of Science.Jan Sprenger & Stephan Hartmann - 2019 - Oxford and New York: Oxford University Press.
Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - British Journal for the Philosophy of Science 68 (2).
Being Realist about Bayes, and the Predictive Processing Theory of Mind.Matteo Colombo, Lee Elkin & Stephan Hartmann - 2021 - British Journal for the Philosophy of Science 72 (1):185-220.

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