Modeling time-varying uncertain situations using Dynamic Influence Nets

AbstractThis paper enhances the Timed Influence Nets (TIN) based formalism to model uncertainty in dynamic situations. The enhancements enable a system modeler to specify persistence and time-varying influences in a dynamic situation that the existing TIN fails to capture. The new class of models is named Dynamic Influence Nets (DIN). Both TIN and DIN provide an alternative easy-to-read and compact representation to several time-based probabilistic reasoning paradigms including Dynamic Bayesian Networks. The Influence Net (IN) based approach has its origin in the Discrete Event Systems modeling. The time delays on arcs and nodes represent the communication and processing delays, respectively, while the changes in the probability of an event at different time instants capture the uncertainty associated with the occurrence of the event over a period of time

Local Probability Distributions in Bayesian Networks: Knowledge Elicitation and Inference

Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowledge and have been applied successfully in many domains for over 25 years. The strength of Bayesian networks lies in the graceful combination of probability theory and a graphical structure representing probabilistic dependencies among domain variables in a compact manner that is intuitive for humans. One major challenge related to building practical BN models is specification of conditional probability distributions. The number of probability distributions in a conditional probability table for a given variable is exponential in its number of parent nodes, so that defining them becomes problematic or even impossible from a practical standpoint. The objective of this dissertation is to develop a better understanding of models for compact representations of local probability distributions. The hypothesis is that such models should allow for building larger models more efficiently and lead to a wider range of BN applications