Computing Upper and Lower Bounds on Likelihoods in Intractable Networks

Abstract

We present deterministic techniques for computing upper and lower bounds on marginal probabilities in sigmoid and noisy-OR networks. These techniques become useful when the size of the network (or clique size) precludes exact computations. We illustrate the tightness of the bounds by numerical experiments.

Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996)

Cited by in corpus (8)

• Variational Inference: A Review for Statisticians
• A General Algorithm for Approximate Inference and its Application to Hybrid Bayes Nets
• Bayesian Evidence and Model Selection
• Structure and Parameter Learning for Causal Independence and Causal Interaction Models
• Using Qualitative Relationships for Bounding Probability Distributions
• Representing and Combining Partially Specified CPTs
• Filtering Variational Objectives
• Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flows