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: Monte Carlo sampling has become a major vehicle for approximate inference in Bayesian networks. In this paper, we investigate a fam­ ily of related simulation approaches, known collectively as quasi-Monte Carlo methods based on deterministic low-discrepancy se­ quences. We first outline several theoreti­ cal aspects of deterministic low-discrepancy sequences, show three examples of such se­ quences, and then discuss practical issues re­ lated to applying them to belief updating in Bayesian networks. We propose an algorithm for selecting direction numbers for Sobol se­ quence. Our experimental results show that low-discrepancy sequences (especially Sobol sequence) significantly improve the perfor­ mance of simulation algorithms in Bayesian networks compared to Monte Carlo sampling.

Computational Investigation of Low-Discrepancy Sequences in Simulation Algorithms for Bayesian Networks