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Proceedings Paper

The neighborhood MCMC sampler for learning Bayesian networks
Author(s): Salem A. Alyami; A. K. M. Azad; Jonathan M. Keith
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Paper 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.

Paper Details

Date Published: 11 July 2016
PDF: 11 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111K (11 July 2016); doi: 10.1117/12.2242708
Show Author Affiliations
Salem A. Alyami, Monash Univ. (Australia)
Al Imam Mohammad Ibn Saud Islamic Univ. (Saudi Arabia)
A. K. M. Azad, Monash Univ. (Australia)
Jonathan M. Keith, Monash Univ. (Australia)


Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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