@inproceedings{706959723d574bf69d9f8f68c1da2268,
title = "“Parallel-Tempering”-Assisted Hybrid Monte Carlo Algorithm for Bayesian Inference in Dynamical Systems",
abstract = "The aim of this work is to tackle the problem of sampling from multi-modal distributions when Hybrid Monte Carlo (HMC) algorithm is employed for performing Bayesian inference in dynamical systems. Hybrid Monte Carlo is a powerful Markov Chain Monte Carlo (MCMC) algorithm but it still suffers from the “multiple peaks” problem. Due to non-trivial structure in the space of (a class of) dynamical systems, posterior distribution of its model parameters could exhibit complicated structures such as multiple ridges. We examined a MCMC algorithm combining HMC with so-called Parallel Tempering (PT) - a well-known strategy for tackling the problem highlighted above. The new algorithm is referred to as PT-HMC. Our numerical experiment demonstrated that when compared to the ground truth, the posterior distributions derived from PT-HMC samples is more accurate than those from HMC.",
keywords = "Dynamical systems, Hybrid Monte Carlo, Multi-modal distribution, Parallel tempering",
author = "Shengjie Sun and Yuan Shen",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 19th Annual UK Workshop on Computational Intelligence, UKCI 2019 ; Conference date: 04-09-2019 Through 06-09-2019",
year = "2020",
doi = "10.1007/978-3-030-29933-0_30",
language = "English",
isbn = "9783030299323",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "357--368",
editor = "Zhaojie Ju and Dalin Zhou and Alexander Gegov and Longzhi Yang and Chenguang Yang",
booktitle = "Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019",
}