An Empirical Study of an Adaptive Langevin Algorithm for Bounded Target Densities

Markov chain Monte Carlo simulation techniques enable the application of Bayesian methods to a variety of models where the posterior density of interest is too difficult to explore analytically. In practice, however, multivariate posterior densities often have characteristics which make implementation of MCMC methods more difficult. A number of techniques have been explored to help speed the convergence of a Markov chain. This paper presents a new algorithm which employs some of these techniques for cases where the target density is bounded. The algorithm is tested on several known distributions to empirically examine convergence properties. It is then applied to a wildlife disease model to demonstrate real-world applicability.

並列關鍵字

Adaptive MCMC ； controlled MCMC ； Langevin algorithms ； Metropolis-Hastings algorithms ； stochastic approximation ； tempered MCMC

被引用紀錄

劉正弘（2010）。中共「反介入戰略」空軍嚇阻能力虛實〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2010.01215

陳玟秀（2006）。團隊新進人員對決策流程滿意度及團隊績效的影響〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2006.01170

Huang, C. C. (2006). 利用平行語料庫與單語樹庫之雙語剖析研究 [master's thesis, National Tsing Hua University]. Airiti Library. https://doi.org/10.6843/NTHU.2006.00134

洪堃哲（2015）。專利授權事業研究-以標準必要專利及RAND授權金為中心〔碩士論文，國立交通大學〕。華藝線上圖書館。https://doi.org/10.6842/NCTU.2015.00868

楊博堯（2014）。我國專利法懲罰性損害賠償制度之現況—以主觀要件為中心〔碩士論文，國立交通大學〕。華藝線上圖書館。https://doi.org/10.6842/NCTU.2014.01035

國際替代計量

An Empirical Study of an Adaptive Langevin Algorithm for Bounded Target Densities

全文下載

主題瀏覽