Intelligent Probabilistic Inference

Understand

The analysis of practical probabilistic models on the computer demands a convenient representation for the available knowledge and an efficient algorithm to perform inference.

• An appealing representation is the influence diagram, a network that makes explicit the random variables in a model and their probabilistic dependencies.
• Recent advances have developed solution procedures based on the influence diagram.
• In this paper, we examine the fundamental properties that underlie those techniques, and the information about the probabilistic structure that is available in the influence diagram representation.