
A Convergence Diagnostic for Bayesian Clustering
Many convergence diagnostics for Markov chain Monte Carlo (MCMC) are wel...
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Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods
Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are findi...
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Markov Chain Monte Carlo sampling for conditional tests: A link between permutation tests and algebraic statistics
We consider conditional tests for nonnegative discrete exponential fami...
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Rateoptimal refinement strategies for local approximation MCMC
Many Bayesian inference problems involve target distributions whose dens...
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Efficient MCMC Sampling with DimensionFree Convergence Rate using ADMMtype Splitting
Performing exact Bayesian inference for complex models is intractable. M...
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Variational MCMC
We propose a new class of learning algorithms that combines variational ...
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Optimal Encoding and Decoding for Point Process Observations: an Approximate ClosedForm Filter
The process of dynamic state estimation (filtering) based on point proce...
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Decayed MCMC Filtering
Filteringestimating the state of a partially observable Markov process from a sequence of observationsis one of the most widely studied problems in control theory, AI, and computational statistics. Exact computation of the posterior distribution is generally intractable for large discrete systems and for nonlinear continuous systems, so a good deal of effort has gone into developing robust approximation algorithms. This paper describes a simple stochastic approximation algorithm for filtering called em decayed MCMC. The algorithm applies Markov chain Monte Carlo sampling to the space of state trajectories using a proposal distribution that favours flips of more recent state variables. The formal analysis of the algorithm involves a generalization of standard coupling arguments for MCMC convergence. We prove that for any ergodic underlying Markov process, the convergence time of decayed MCMC with inversepolynomial decay remains bounded as the length of the observation sequence grows. We show experimentally that decayed MCMC is at least competitive with other approximation algorithms such as particle filtering.
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