
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference
In this contribution, we propose a new computationally efficient method ...
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Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal Lkernels
By facilitating the generation of samples from arbitrary probability dis...
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Multiple projection MCMC algorithms on submanifolds
We propose new Markov Chain Monte Carlo algorithms to sample probability...
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Diffusion approximations and control variates for MCMC
A new methodology is presented for the construction of control variates ...
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Wastefree Sequential Monte Carlo
A standard way to move particles in a SMC sampler is to apply several st...
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Decayed MCMC Filtering
Filteringestimating the state of a partially observable Markov proces...
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Converting CascadeCorrelation Neural Nets into Probabilistic Generative Models
Humans are not only adept in recognizing what class an input instance be...
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Variational MCMC
We propose a new class of learning algorithms that combines variational approximation and Markov chain Monte Carlo (MCMC) simulation. Naive algorithms that use the variational approximation as proposal distribution can perform poorly because this approximation tends to underestimate the true variance and other features of the data. We solve this problem by introducing more sophisticated MCMC algorithms. One of these algorithms is a mixture of two MCMC kernels: a random walk Metropolis kernel and a blockMetropolisHastings (MH) kernel with a variational approximation as proposaldistribution. The MH kernel allows one to locate regions of high probability efficiently. The Metropolis kernel allows us to explore the vicinity of these regions. This algorithm outperforms variationalapproximations because it yields slightly better estimates of the mean and considerably better estimates of higher moments, such as covariances. It also outperforms standard MCMC algorithms because it locates theregions of high probability quickly, thus speeding up convergence. We demonstrate this algorithm on the problem of Bayesian parameter estimation for logistic (sigmoid) belief networks.
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