A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modeling Techniques
This work addresses the need for improved temporal modeling in probabilistic graphical models, but it appears incremental as it builds directly on existing Bayesian Belief Networks.
The authors tackled the problem of temporal and causal modeling under uncertainty by developing Modifiable Temporal Belief Networks (MTBNs) as an extension of Bayesian Belief Networks, presenting definitions, properties, and modeling techniques without providing concrete numerical results.
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.