Context-Specific Approximation in Probabilistic Inference

Understand

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.