Non-stationary continuous dynamic Bayesian networks
Marco Andreas Grzegorczyk, Dirk Husmeier · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · 2009
Dynamic Bayesian networks have been applied widely to reconstruct the structure of regulatory processes from time series data.The standard approach is based on the assumption of a homogeneous Markov chain, which is not valid in many realworld scenarios.Recent research efforts addressing this shortcoming have considered undirected graphs, directed graphs for discretized data, or over-flexible models that lack any information sharing among time series segments.In the present article, we propose a non-stationary dynamic Bayesian network for continuous data, in which parameters are allowed to vary among segments, and in which a common network structure provides essential information sharing across segments.Our model is based on a Bayesian multiple change-point process, where the number and location of the change-points is sampled from the posterior distribution.