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: A Bayesian Belief Network (BN) is a model of a joint distribution over a finite set of variables, with a DAG structure to represent the immedi­ ate dependencies between the variables, and a set of parameters (aka CPTables) to represent the local conditional probabilities of a node, given each assignment to its parents. In many situa­ tions, the parameters are themselves treated as random variablesreflecting the uncertainty re­ maining after drawing on knowledge of domain experts and/or observing data generated by the network. A distribution over the CPtable param­ eters induces a distribution for the response the BN will return to any "What is Pr{ H I E} ?" query. This paper investigates the distribution of this response, shows that it is asymptotically normal, and derives expressions for its mean and asymptotic variance. We show that this compu­ tation has the same complexity as simply com­ puting the (mean value of the) response -i.e., O(n exp(w)), where n is the number of vari­ ables and w is the effective tree width. We also provide empirical evidence showing that the error-bars computed from our estimates are fairly accurate in practice, over a wide range of belief net structures and queries.

Bayesian Error-Bars for Belief Net Inference