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The method proposed uses hitherto unknown properties of the gradient of the underlying log-empirical-likelihood function. We use results from convex analysis to show that these properties hold under minimal assumptions on the parameter space, prior density and the functions used in the estimating equations determining the empirical likelihood. Our method employs a finite number of estimating equations and observations but produces valid semiparametric inference for a large class of statistical models including mixed effects models, generalized linear models and hierarchical Bayes models. We overcome major challenges posed by complex, non-convex boundaries of the support routinely observed for empirical likelihood which prevent efficient implementation of traditional Markov chain Monte Carlo methods like random-walk Metropolis\u2013Hastings sampling etc. with or without parallel tempering. A simulation study confirms that our method converges quickly and draws samples from the posterior support efficiently. We further illustrate its utility through an analysis of a discrete data set in small area estimation.<\/jats:p>","DOI":"10.1111\/rssb.12164","type":"journal-article","created":{"date-parts":[[2016,4,10]],"date-time":"2016-04-10T18:56:31Z","timestamp":1460314591000},"page":"293-320","source":"Crossref","is-referenced-by-count":34,"title":["Hamiltonian Monte Carlo Sampling in Bayesian Empirical Likelihood Computation"],"prefix":"10.1093","volume":"79","author":[{"given":"Sanjay","family":"Chaudhuri","sequence":"first","affiliation":[{"name":"National University of Singapore \u00a0 Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debashis","family":"Mondal","sequence":"additional","affiliation":[{"name":"Oregon State University , Corvallis , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teng","family":"Yin","sequence":"additional","affiliation":[{"name":"NCS Private \u00a0 Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2016,4,7]]},"reference":[{"key":"2023021801101961600_rssb12164-cit-0001","volume-title":"Topological Spaces: Including a Treatment of Multi-valued Functions, Vector Spaces, and Convexity","author":"Berge","year":"1963"},{"key":"2023021801101961600_rssb12164-cit-0002","doi-asserted-by":"crossref","first-page":"2356","DOI":"10.1214\/12-EJS750","article-title":"The empty set and zero likelihood problems in maximum empirical likelihood estimation","volume":"6","author":"Bergsma","year":"2012","journal-title":"Electron. 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