A General Algorithm for Approximate Inference and its Application to Hybrid Bayes Nets

Abstract

The clique tree algorithm is the standard method for doing inference in Bayesian networks. It works by manipulating clique potentials - distributions over the variables in a clique. While this approach works well for many networks, it is limited by the need to maintain an exact representation of the clique potentials. This paper presents a new unified approach that combines approximate inference and the clique tree algorithm, thereby circumventing this limitation. Many known approximate inference algorithms can be viewed as instances of this approach. The algorithm essentially does clique tree propagation, using approximate inference to estimate the densities in each clique. In many settings, the computation of the approximate clique potential can be done easily using statistical importance sampling. Iterations are used to gradually improve the quality of the estimation.

Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)

References in corpus (4)

• Stochastic Simulation Algorithms for Dynamic Probabilistic Networks
• Nonuniform Dynamic Discretization in Hybrid Networks
• Computing Upper and Lower Bounds on Likelihoods in Intractable Networks
• HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees

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