Tradeoffs in knowledge-based construction of probabilistic models

In many domains, the ability to use a knowledge base to automatically construct alternative probabilistic network models and then compare them is desirable. This paper makes two novel contributions towards achieving that goal: first, it analyzes a parameterized class of (a) static, and (b) temporal influence diagram, models which differ in the time-series process describing the temporal evolution of the system being modeled. Second, it applies general scoring metrics for comparing these models with respect to predictive accuracy and computational efficiency. The network rankings facilitate comparing the accuracy/efficiency tradeoffs entailed in using TIDs which differ in (1) the accuracy of capturing the temporal evolution of a dynamic system and (2) data and computational requirements. The scoring metrics are used to compare networks in which all variables evolve according to a Markov process with two novel domain-dependent network approximations. These approximations model the evolution of a parsimonious subset of variables rather than all variables