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Accelerated Adaptive Markov Chain for Partition Function Computation
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman
We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function. In particular, we show how to accelerate a flat histogram sampling technique by significantly reducing the number of ``null moves'' in the chain, while maintaining asymptotic convergence properties. Our experiments show that our method converges quickly to highly accurate solutions on a range of benchmark instances, outperforming other state-of-the-art methods such as IJGP, TRW, and Gibbs sampling both in run-time and accuracy. We also show how obtaining a so-called density of states distribution allows for efficient weight learning in Markov Logic theories.
Author Information
Stefano Ermon (Stanford University)
Carla Gomes (Cornell University)
Ashish Sabharwal (IBM Watson Research Center)
Bart Selman (Cornell University)
Related Events (a corresponding poster, oral, or spotlight)
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2011 Poster: Accelerated Adaptive Markov Chain for Partition Function Computation »
Tue Dec 13th 04:45 -- 10:59 PM Room None
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