10.4225/03/596bf0b5531eb
SALEM ALI S ALYAMI
SALEM ALI S
ALYAMI
Markov chain Monte Carlo methods for Bayesian network inference, with applications in systems biology
Monash University
2017
Bayesian networks
Markov chain Monte Carlo
Structure learning
Graph space
Sampling
Systems biology
Bayesian software
Statistics
Applied Discrete Mathematics
Biostatistics
2017-07-16 23:03:14
Thesis
https://bridges.monash.edu/articles/thesis/Markov_chain_Monte_Carlo_methods_for_Bayesian_network_inference_with_applications_in_systems_biology/5208175
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.