Constant-space reasoning in dynamic Bayesian networks

Constant-space reasoning in dynamic Bayesian networks

Reasoning about Constant Coefficient Dynamic Systems

One of the main objectives of qualitative physics is to derive the behavior of a system from the description of its components and their interrelationships. Observation of the problems that qualitative simulation techniques present, motivated us to study physical systems from adifferent perspective, their analytical solutions. In this paper we present a framework to reason about dynamic systems...

Graphical Reasoning with Bayesian Networks

Nowadays, Bayesian networks are seen by many researchers as standard tools for reasoning with uncertainty. Despite the fact that Bayesian networks are graphical representations, representing dependence and independence information, normally the emphasis of the visualisation of the reasoning process is on showing changes in the associated marginal probability distributions due to entering observ...

Temporal Reasoning and Bayesian Networks

This work examines important issues in probabilistic temporal representation and reasoning using Bayesian networks (also known as belief networks). The representation proposed here utilizes temporal (or dynamic) probabilities to represent facts, events, and the effects of events. The architecture of a belief network may change with time to indicate a different causal context. Probability variat...

Bayesian Networks for Logical Reasoning

By identifying and pursuing analogies between causal and logical influence I show how the Bayesian network formalism can be applied to reasoning about logical deductions.