Context-Specific Approximation in Probabilistic Inference

Abstract

There is evidence that the numbers in probabilistic inference don't really matter. This paper considers the idea that we can make a probabilistic model simpler by making fewer distinctions. Unfortunately, the level of a Bayesian network seems too coarse; it is unlikely that a parent will make little difference for all values of the other parents. In this paper we consider an approximation scheme where distinctions can be ignored in some contexts, but not in other contexts. We elaborate on a notion of a parent context that allows a structured context-specific decomposition of a probability distribution and the associated probabilistic inference scheme called probabilistic partial evaluation (Poole 1997). This paper shows a way to simplify a probabilistic model by ignoring distinctions which have similar probabilities, a method to exploit the simpler model, a bound on the resulting errors, and some preliminary empirical results on simple networks.

Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)

References in corpus (1)

• Context-Specific Independence in Bayesian Networks

Cited by in corpus (8)

• Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
• When do Numbers Really Matter?
• Learning Bayesian Networks with Restricted Causal Interactions
• Representing and Combining Partially Specified CPTs
• When do Numbers Really Matter?
• Active Tuples-based Scheme for Bounding Posterior Beliefs
• CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
• Context-Specific Likelihood Weighting