A Particle Gibbs Sampling Approach To Topology Inference In Gene Regulatory Networks

In this paper, we propose a novel Bayesian approach for estimating a gene network's topology using particle Gibbs sampling. The conditional posterior distributions of the unknowns in a state-space model describing the time evolution of gene expressions are derived and employed for exact Bayesian posterior inference. Specifically, the proposed scheme provides the joint posterior distribution of the unknown gene expressions, the adjacency matrix describing the topology of the network, and the coefficient matrix describing the strength of the gene interactions. We validate the proposed method with numerical simulations on synthetic data experiments.