Detailed 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.
Q: 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.
A:
Computational Investigation of Low-Discrepancy Sequences in Simulation Algorithms for Bayesian Networks