19 research outputs found
Variational Probabilistic Inference and the QMR-DT Network
We describe a variational approximation method for efficient inference in
large-scale probabilistic models. Variational methods are deterministic
procedures that provide approximations to marginal and conditional
probabilities of interest. They provide alternatives to approximate inference
methods based on stochastic sampling or search. We describe a variational
approach to the problem of diagnostic inference in the `Quick Medical
Reference' (QMR) network. The QMR network is a large-scale probabilistic
graphical model built on statistical and expert knowledge. Exact probabilistic
inference is infeasible in this model for all but a small set of cases. We
evaluate our variational inference algorithm on a large set of diagnostic test
cases, comparing the algorithm to a state-of-the-art stochastic sampling
method
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Local expression languages for probabilistic dependence
A 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 micro-structure 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