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