Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning.
Bayesian networks have become popular for modeling probabilistic relationships between entities. As their structure can also be given a causal interpretation about the studied system, they can be used to learn, for example, regulatory relationships of genes or proteins in biological networks and pathways. Inference of the Bayesian network structure is complicated by the size of the model structure space, necessitating the use of optimization methods or sampling techniques, such Markov Chain Monte Carlo (MCMC) methods. However, convergence of MCMC chains is in many cases slow and can become even a harder issue as the dataset size grows. We show here how to improve convergence in the Bayesian network structure space by using an adjustable proposal distribution with the possibility to propose a wide range of steps in the structure space, and demonstrate improved network structure inference by analyzing phosphoprotein data from the human primary T cell signaling network.
Department of Information and Computer Science, Aalto University, FI-00076Aalto, Finland.|Department of Signal Processing, Tampere University of Technology, Tampere, FI-33101 Finland.
Antti
A
Larjo
Department of Information and Computer Science, Aalto University, FI-00076Aalto, Finland.|Turku Centre for Biotechnology, Turku University, Turku, FI-20521 Finland.
Harri
H
Lähdesmäki
Bayesian network; MCMC; Proposal distribution; Structure learning