Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints

Abstract

In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid Bayesian Networks that allow discrete deterministic information to be modeled explicitly in the form of constraints. We present two approximate inference algorithms for HMNs that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling and Constraint Propagation to address the complexity of modeling and reasoning in HMNs. We demonstrate the performance of our approximate inference algorithms on randomly generated HMNs.

Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)

References in corpus (3)

• Iterative Join-Graph Propagation
• A Simple Insight into Iterative Belief Propagation's Success
• An Empirical Study of w-Cutset Sampling for Bayesian Networks

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• Formula-Based Probabilistic Inference
• Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
• Inference in Hybrid Bayesian Networks Using Mixtures of Gaussians
• AND/OR Importance Sampling
• Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality
• Measuring the Hardness of Stochastic Sampling on Bayesian Networks with Deterministic Causalities: the k-Test