Representation via Probabilities: Models of Probabilistic Modeling

Beisbart, Claus (12 October 2013). Representation via Probabilities: Models of Probabilistic Modeling (Unpublished). In: Probabilistic Modeling in Science and Philosophy. Bern. 12.10.2013.

How do probabilistic models represent their targets and how do they allow us to learn about them? The answer to this question depends on a number of details, in particular on the meaning of the probabilities involved. To classify the options, a minimalist conception of representation (Su\'arez 2004) is adopted: Modelers devise substitutes (``sources'') of their targets and investigate them to infer something about the target. Probabilistic models allow us to infer probabilities about the target from probabilities about the source. This leads to a framework in which we can systematically distinguish between different models of probabilistic modeling. I develop a fully Bayesian view of probabilistic modeling, but I argue that, as an alternative, Bayesian degrees of belief about the target may be derived from ontic probabilities about the source. Remarkably, some accounts of ontic probabilities can avoid problems if they are supposed to apply to sources only.

Item Type:

Conference or Workshop Item (Speech)


06 Faculty of Humanities > Department of Art and Cultural Studies > Institute of Philosophy

UniBE Contributor:

Beisbart, Claus


100 Philosophy
100 Philosophy > 120 Epistemology
500 Science




Claus Beisbart

Date Deposited:

23 Apr 2014 12:59

Last Modified:

23 Apr 2014 12:59

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