The neighborhood MCMC sampler for learning Bayesian networks

Salem A. Alyami; A. K. M. Azad; Jonathan M. Keith

doi:10.1117/12.2242708

11 July 2016 The neighborhood MCMC sampler for learning Bayesian networks

Salem A. Alyami, A. K. M. Azad, Jonathan M. Keith

Proceedings Volume 10011, First International Workshop on Pattern Recognition; 100111K (2016) https://doi.org/10.1117/12.2242708
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan

Abstract

Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) structures. In this paper, we studied a recently proposed Markov chain Monte Carlo (MCMC) sampler, called the Neighbourhood sampler (NS), and examined how efficiently it can sample BNs when local maxima are present. We assume that a posterior distribution f(N,E|D) has been defined, where D represents data relevant to the inference, N and E are the sets of nodes and directed edges, respectively. We illustrate the new approach by sampling from such a distribution, and inferring BNs. The simulations conducted in this paper show that the new learning approach substantially avoids getting stuck in local modes of the distribution, and achieves a more rapid rate of convergence, compared to other common algorithms e.g. the MCMC Metropolis-Hastings sampler.

Citation Download Citation

Salem A. Alyami, A. K. M. Azad, and Jonathan M. Keith "The neighborhood MCMC sampler for learning Bayesian networks", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111K (11 July 2016); https://doi.org/10.1117/12.2242708

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