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Foundations for Bayesian networks
, 2001
"... Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probabi ..."
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Cited by 11 (7 self)
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Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current foundations are problematic, and put forward new foundations which involve aspects of both the interpreted and the formal approaches. One standard approach is to interpret a Bayesian network objectively: the graph in a Bayesian network represents causality in the world and the specified probabilities are objective, empirical probabilities. Such an interpretation founders when the Bayesian network independence assumption (often called the causal Markov condition) fails to hold. In §2 I catalogue the occasions when the independence assumption fails, and show that such failures are pervasive. Next, in §3, I show that even where the independence assumption does hold objectively, an agent’s causal knowledge is unlikely to satisfy the assumption with respect to her subjective probabilities, and that slight differences between an agent’s subjective Bayesian network and an objective Bayesian network can lead to large differences between probability distributions determined by these networks. To overcome these difficulties I put forward logical Bayesian foundations in §5. I show that if the graph and probability specification in a Bayesian network are thought of as an agent’s background knowledge, then the agent is most rational if she adopts the probability distribution determined by the
Philosophies of probability: objective Bayesianism and its challenges
 Handbook of the philosophy of mathematics. Elsevier, Amsterdam. Handbook of the Philosophy of Science
, 2004
"... 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. ..."
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Cited by 10 (5 self)
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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.
Philosophies of probability
 Handbook of the Philosophy of Mathematics, Volume 4 of the Handbook of the Philosophy of Science
"... 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 ..."
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Cited by 6 (2 self)
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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.
INSTITUT DES SCIENCES ET TECHNOLOGIES École doctorale n O 432: SMI Sciences des Métiers de l’ingénieur
"... pour obtenir le grade de docteur délivré par ..."
Why It is Common Sense to Invent Reasons
, 2003
"... This paper is an o shoot of a much larger project which started about 17 years ago with an attempt to construct an expert system for medical diagnosis. The idea, and such ideas were common at that time, and probably still are for all we know, was to program a computer so that it would be enough to t ..."
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This paper is an o shoot of a much larger project which started about 17 years ago with an attempt to construct an expert system for medical diagnosis. The idea, and such ideas were common at that time, and probably still are for all we know, was to program a computer so that it would be enough to type in the patient's signs, symptoms, personal details etc. and the computer would come up with a quali ed diagnosis, just in the way a doctor appears to do
INTERPRETATION, COORDINATION AND CONFORMITY
, 2005
"... Abstract The aim of this note is to investigate a very general problem of (radical) interpretation in terms of a simple coordination game: the conformity game. We show how, within our mathematical framework, the solution concept for the conformity game does indeed provide an algorithmic procedure fa ..."
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Abstract The aim of this note is to investigate a very general problem of (radical) interpretation in terms of a simple coordination game: the conformity game. We show how, within our mathematical framework, the solution concept for the conformity game does indeed provide an algorithmic procedure facilitating triangulation, in the sense of Davidson.
§3 Goodman’s New Problem of Induction 7
"... Journal of Logic, Language and Information, to appear Bayesian probability is normally defined over a fixed language or event space. But in practice language is susceptible to change, and the question naturally arises as to how Bayesian degrees of belief should change as language changes. I argue he ..."
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Journal of Logic, Language and Information, to appear Bayesian probability is normally defined over a fixed language or event space. But in practice language is susceptible to change, and the question naturally arises as to how Bayesian degrees of belief should change as language changes. I argue here that this question poses a serious challenge to Bayesianism. The Bayesian may be able to meet this challenge however, and I outline a practical method for changing degrees of belief over changes in