Bidirectional inference in probabilistic temporal reasoning

Belief update based on observations is a fundamental problem in AI. We propose a belief update strategy suitable for temporal probabilistic domains. We assume that the causal structure of the world is specified in probabilistic terms. The causal specifications are used to perform two basic operations: explanation (backward propagation) and projection (forward propagation). We show that probabilistic belief update results from interleaving explanation and projection.