Expressive Probability Models in Science

The paper sketches an approximate inference method for representation systems of this kind and suggests that this further expansion should combine probability theory with the expressive power of first-order logical languages.

Abstract

The paper is a brief summary of an invited talk given at the Discovery Science conference. The principal points are as follows: first, that probability theory forms the basis for connecting hypotheses and data; second, that the expressive power of the probability models used in scientific theory formation has expanded significantly; and finally, that still further expansion is required to tackle many problems of interest. This further expansion should combine probability theory with the expressive power of first-order logical languages. The paper sketches an approximate inference method for representation systems of this kind.