Solving Hybrid Influence Diagrams with Deterministic Variables

Details Solving Hybrid Influence Diagrams with Deterministic Variables Solving Hybrid Influence Diagrams with Deterministic Variables

2012-03-15

arxiv.org/abs/1203.3493v1

Computer Science

Artificial Intelligence

Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)

Scientific paper

We describe a framework and an algorithm for solving hybrid influence diagrams with discrete, continuous, and deterministic chance variables, and discrete and continuous decision variables. A continuous chance variable in an influence diagram is said to be deterministic if its conditional distributions have zero variances. The solution algorithm is an extension of Shenoy's fusion algorithm for discrete influence diagrams. We describe an extended Shenoy-Shafer architecture for propagation of discrete, continuous, and utility potentials in hybrid influence diagrams that include deterministic chance variables. The algorithm and framework are illustrated by solving two small examples.

Affiliated with

Also associated with

No associations

Rating

Solving Hybrid Influence Diagrams with Deterministic Variables does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Solving Hybrid Influence Diagrams with Deterministic Variables, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Solving Hybrid Influence Diagrams with Deterministic Variables will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-32225