1,762 research outputs found
Cutset Sampling for Bayesian Networks
The paper presents a new sampling methodology for Bayesian networks that
samples only a subset of variables and applies exact inference to the rest.
Cutset sampling is a network structure-exploiting application of the
Rao-Blackwellisation principle to sampling in Bayesian networks. It improves
convergence by exploiting memory-based inference algorithms. It can also be
viewed as an anytime approximation of the exact cutset-conditioning algorithm
developed by Pearl. Cutset sampling can be implemented efficiently when the
sampled variables constitute a loop-cutset of the Bayesian network and, more
generally, when the induced width of the networks graph conditioned on the
observed sampled variables is bounded by a constant w. We demonstrate
empirically the benefit of this scheme on a range of benchmarks
Mean Field Theory for Sigmoid Belief Networks
We develop a mean field theory for sigmoid belief networks based on ideas
from statistical mechanics. Our mean field theory provides a tractable
approximation to the true probability distribution in these networks; it also
yields a lower bound on the likelihood of evidence. We demonstrate the utility
of this framework on a benchmark problem in statistical pattern
recognition---the classification of handwritten digits.Comment: See http://www.jair.org/ for any accompanying file
Blocking Gibbs sampling in the mixed inheritance model using graph theory
International audienc
Parallel Adaptive Collapsed Gibbs Sampling
Rao-Blackwellisation is a technique that provably improves the performance of Gibbs sampling by summing-out variables from the PGM. However, collapsing variables is computationally expensive, since it changes the PGM structure introducing factors whose size is dependent upon the Markov blanket of the variable. Therefore, collapsing out several variables jointly is typically intractable in arbitrary PGM structures. This thesis proposes an adaptive approach for Rao-Blackwellisation, where additional parallel Markov chains are defined over different collapsed PGM structures. The collapsed variables are chosen based on their convergence diagnostics. Adding chains requires re-burn-in the chain, thus wasting samples. To address this, new chains are initialized from a mean field approximation for the distribution, that improves over time, thus reducing the burn-in period. The experiments on several UAI benchmarks shows that this approach is more accurate than state-of-the-art inference systems such as Merlin which have previously won the UAI inference challenge
- …