5208175_PhDThesis_SalemALYAMIID23709987.pdf (12.05 MB)
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Markov chain Monte Carlo methods for Bayesian network inference, with applications in systems biology

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posted on 16.07.2017, 23:03 by SALEM ALI S ALYAMI
Getting stuck in local maxima is a problem that arises while inferring Bayesian network (BN) structures. This thesis proposes Markov chain Monte Carlo (MCMC) samplers that have been known to substantially resolve this problem but have not been modified to fit simulating BN structures from discrete search spaces. The proposed MCMC samplers are new instances of the Neighbourhood sampler, Hit-and-Run sampler and Metropolis-Hastings sampler. Two adaptive techniques have been also developed to reduce the time-complexity required for inference. A new software has been designed to facilitate using the samplers. The proposed samplers have demonstrated efficient performance in practice.


Campus location


Principal supervisor

Jonathan Macgregor Keith

Additional supervisor 1

Tim Garoni

Additional supervisor 2

Catherine Forbes

Year of Award


Department, School or Centre

Mathematical Sciences


Doctor of Philosophy

Degree Type



Faculty of Science