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Developments in Exact Inference in Graphical Models

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If you have a question about this talk, please contact Zoubin Ghahramani.

My talk is split into two parts:

Part 1 - I develop an algorithm for dynamic updating and marginalisation in tree-structured Markov Random Fields. This algorithm is always at least as fast as any other algorithm and in some cases is exponentially faster than any other algorithm. The initialisation time required by the algorithm is the optimal, linear time.
Part 2 - The junction tree algorithm can be slow when we have large supernodes of high degree and when we have such supernodes the difference in time complexity between different architectures becomes very obvious. Previously, the fastest junction tree algorithms had a very large memory requirement. I develop a junction tree architecture which has, essentially, state of the art speed (faster than HUGIN propagation) while essentially maintaining the low space requirement of the slower Shafer-Shennoy propagation.

This talk is part of the Machine Learning @ CUED series.

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