Approximate Learning in Complex Dynamic Bayesian Networks

TL;DR

This paper develops fast approximate Bayesian algorithms for learning in complex, dynamic Bayesian networks with non-Gaussian observations, combining recent forecasting methods with Gaussian propagation techniques.

Contribution

It introduces a novel methodology for efficient learning in complex dynamic Bayesian networks, extending previous work with new algorithms and theoretical analysis.

Findings

Effective approximation of Bayesian learning in complex networks

Theoretical bounds on approximation efficacy

Application to real-world data demonstrating practicality

Abstract

In this paper we extend the work of Smith and Papamichail (1999) and present fast approximate Bayesian algorithms for learning 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 developments in approximate Bayesian forecasting methods in combination with more familiar Gaussian propagation algorithms on junction trees. The procedure for learning state parameters from data is given explicitly for common sampling distributions and the methodology is illustrated through a real application. The efficiency of the dynamic approximation is explored by using the Hellinger divergence measure and theoretical bounds for the efficacy of such a procedure are discussed.