Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: In this paper, we use evidence-specific value ab­ straction for speeding Bayesian networks infer­ ence. This is done by grouping variable val­ ues and treating the combined values as a sin­ gle entity. As we show, such abstractions can ex­ ploit regularities in conditional probability distri­ butions and also the specific values of observed variables. To formally justify value abstraction, we defi ne the notion of safe value abstraction and devise inference algorithms that use it to re­ duce the cost of inference. Our procedure is par­ ticularly useful for learning complex networks with many hidden variables. In such cases, re­ peated likelihood computations are required for E M or other parameter optimization techniques. Since these computations are repeated with re­ spect to the same evidence set, our methods can provide signifi cant speedup to the learning pro­ cedure. We demonstrate the algorithm on genetic linkage problems where the use of value abstrac­ tion sometimes differentiates between a feasible and non-feasible solution.

Likelihood Computations Using Value Abstraction