Mar 1, 2010 · Title:Free Energy Methods for Bayesian Inference: Efficient Exploration of Univariate Gaussian Mixture Posteriors ; Subjects: Computation (stat.
Jun 23, 2011 · Free energy methods for Bayesian inference: efficient exploration of univariate Gaussian mixture posteriors · Abstract · Article PDF · References.
PDF | Because of their multimodality, mixture posterior distributions are difficult to sample with standard Markov chain Monte Carlo (MCMC) methods. We.
This work uses adaptive biasing Markov chain algorithms which adapt their targeted invariant distribution on the fly, in order to overcome sampling barriers ...
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Apr 18, 2011 · Free Energy Methods for Bayesian Inference: Efficient. Exploration of Univariate Gaussian Mixture Posteriors. Nicolas Chopin1∗, Tony Leli ...
Free Energy Methods for Efficient Exploration of Mixture Posterior ...
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We propose a strategy to enhancethe sampling of MCMC in this context, using a biasing procedure which originates fromcomputational statistical physics. The ...
Missing: inference: univariate
Free energy methods for Bayesian inference: efficient exploration of univariate Gaussian mixture posteriors. N Chopin, T Lelièvre, G Stoltz. Statistics and ...
We study the problem of approximate sampling from non-log-concave distributions, e.g., Gaussian mixtures, which is often challenging even in low dimensions due ...
Abstract. We introduce a new class of Sequential Monte Carlo (SMC) methods, which we call free energy SMC. This class is inspired by free energy methods, w.
Sep 29, 2019 · In this paper, we propose using Bayesian sequential Monte Carlo (SMC) algorithm to estimate the univariate Gaussian mixture autoregressive (GMAR) ...