A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modeling Techniques

TL;DR

This paper introduces Modifiable Temporal Belief Networks (MTBNs), an extension of Bayesian Belief Networks that incorporates structural and temporal modeling for better handling of uncertainty in dynamic causal systems.

Contribution

It defines the MTBN model, explores its properties, and discusses techniques for representing complex temporal and causal knowledge, including hybrid and dynamic structures.

Findings

MTBNs effectively model complex temporal and causal relationships.

The paper clarifies the relationship between BNs, Modifiable Belief Networks, and MTBNs.

Guidelines for representing various types of temporal knowledge are provided.

Abstract

We developed the language of Modifiable Temporal Belief Networks (MTBNs) as a structural and temporal extension of Bayesian Belief Networks (BNs) to facilitate normative temporal and causal modeling under uncertainty. In this paper we present definitions of the model, its components, and its fundamental properties. We also discuss how to represent various types of temporal knowledge, with an emphasis on hybrid temporal-explicit time modeling, dynamic structures, avoiding causal temporal inconsistencies, and dealing with models that involve simultaneously actions (decisions) and causal and non-causal associations. We examine the relationships among BNs, Modifiable Belief Networks, and MTBNs with a single temporal granularity, and suggest areas of application suitable to each one of them.