LOCAL EXPRESSION LANGUAGES FOR PROBABILISTIC DEPENDENCE

A Bayesian belief net is a factored representation for a joint probability distribution over a set of variables. This factoring is made possible by the conditional independence relationships among variables made evident in the sparseness of the graphical level of the net. There is, however, another source of factoring available which cannot be directly represented in this graphical structure. This source is the microstructure within an individual marginal or conditional distribution. We present a representation capable of making this intradistribution structure explicit, and an extension to the SPI algorithm capable of utilizing this structural information to improve the efficiency of inference. We discuss the expressivity of the local expression language, and present early experimental results showing the efficacy of the approach.