Bayesian Generalised Ensemble Markov Chain Monte Carlo
Abstract
Bayesian generalised ensemble (BayesGE) is a new method that addresses two major drawbacks of standard Markov chain Monte Carlo algorithms for inference in high-dimensional probability models: inapplicability to estimate the partition function and poor mixing properties. BayesGE uses a Bayesian approach to iteratively update the belief about the density of states (distribution of the log likelihood under the prior) for the model, with the dual purpose of enhancing the sampling efficiency and making the estimation of the partition function tractable. We benchmark BayesGE on Ising and Potts systems and show that it compares favourably to existing state-of-the-art methods.
Cite
Text
Frellsen et al. "Bayesian Generalised Ensemble Markov Chain Monte Carlo." International Conference on Artificial Intelligence and Statistics, 2016.Markdown
[Frellsen et al. "Bayesian Generalised Ensemble Markov Chain Monte Carlo." International Conference on Artificial Intelligence and Statistics, 2016.](https://mlanthology.org/aistats/2016/frellsen2016aistats-bayesian/)BibTeX
@inproceedings{frellsen2016aistats-bayesian,
title = {{Bayesian Generalised Ensemble Markov Chain Monte Carlo}},
author = {Frellsen, Jes and Winther, Ole and Ghahramani, Zoubin and Ferkinghoff-Borg, Jesper},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2016},
pages = {408-416},
url = {https://mlanthology.org/aistats/2016/frellsen2016aistats-bayesian/}
}