Recursive Algorithms for Approximating Probabilities in Graphical Models

We develop a recursive node-elimination formalism for efficiently approximating large probabilistic networks. No constraints are set on the network topologies. Yet the formalism can be straightfor(cid: 173) wardly integrated with exact methods whenever they are/become applicable. The approximations we use are controlled: they main(cid: 173) tain consistently upper and lower bounds on the desired quantities at all times. We show that Boltzmann machines, sigmoid belief networks, or any combination (i. e. , chain graphs) can be handled within the same framework. The accuracy of the methods is veri(cid: 173) fied experimentally.

Authors

• Tommi Jaakkola
• Michael Jordan

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