Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons

TL;DR

This paper provides a comprehensive overview of learning Dynamic Bayesian Networks from data, covering theoretical foundations, model forms, learning methods, and numerical comparisons of algorithms.

Contribution

It offers a formal framework, analytical models, and a categorization of learning methods for DBNs, along with comparative analysis across different algorithms.

Findings

Analytical forms of likelihood and Bayesian scores for DBNs

Discussion on structure and weight interdependence in learning

Numerical comparisons of various learning algorithms

Abstract

In this paper, we present a guide to the foundations of learning Dynamic Bayesian Networks (DBNs) from data in the form of multiple samples of trajectories for some length of time. We present the formalism for a generic as well as a set of common types of DBNs for particular variable distributions. We present the analytical form of the models, with a comprehensive discussion on the interdependence between structure and weights in a DBN model and their implications for learning. Next, we give a broad overview of learning methods and describe and categorize them based on the most important statistical features, and how they treat the interplay between learning structure and weights. We give the analytical form of the likelihood and Bayesian score functions, emphasizing the distinction from the static case. We discuss functions used in optimization to enforce structural requirements. We briefly discuss more complex extensions and representations. Finally we present a set of comparisons in different settings for various distinct but representative algorithms across the variants.