Instructions: 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: A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for learning BNs, however, attempt to optimize another criterion (usu ally likelihood, possibly augmented with a regularizing term) , which is independent of the distribution of queries that are posed. This paper takes the "performance criteria" seriously, and considers the challenge of com puting the BN whose performance read "accuracy over the distribution of queries" is optimal. We show that many aspects of this learning task are more difficult than the corresponding subtasks in the standard model.
Output:
Learning Bayesian Nets that Perform Well