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Learning Conditional Information
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AI
DOI：10.1111/j.1468-0017.2012.01443.x.png)
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Abstract
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Some of the information we receive comes to us in an explicitly conditional form. It is an open question how to model the accommodation of such information in a Bayesian framework. This paper presents data suggesting that there may be no strictly Bayesian account of updating on conditionals. Specifically, the data seem to indicate that such updating at least sometimes proceeds on the basis of explanatory considerations, which famously have no home in standard Bayesian epistemology. The paper also proposes a still broadly Bayesian model of updating on conditionals that explicitly factors in explanation. The model is shown to have clear empirical content and thus to be open to empirical testing.
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