Approximate Learning in Complex Dynamic Bayesian Networks

In this paper we extend the work of Smith and Papamichail (1999) and present fast approximate Bayesian algorithms for learn­ ing in complex scenarios where at any time frame, the relationships between explanatory state space variables can be described by a Bayesian network that evolve dynamically over time and the observations taken are not necessarily Gaussian. It uses recent devel­ opments in approximate Bayesian forecast­ ing methods in combination with more fa­ miliar Gaussian propagation algorithms on junction trees. The procedure for learn­ ing state parameters from data is given ex­ plicitly for common sampling distributions and the methodology is illustrated through a real application. The efficiency of the dy­ namic approximation is explored by using the Hellinger divergence measure and theoretical bounds for the efficacy of such a procedure are discussed.