{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:31:39Z","timestamp":1761597099674,"version":"3.35.0"},"reference-count":28,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2008,8]]},"abstract":"<jats:p>In this paper, we study Bayesian analysis of nonlinear hierarchical mixture models with a finite but unknown number of components. Our approach is based on Markov chain Monte Carlo (MCMC) methods. One of the applications of our method is directed to the clustering problem in gene expression analysis. From a mathematical and statistical point of view, we discuss the following topics: theoretical and practical convergence problems of the MCMC method; determination of the number of components in the mixture; and computational problems associated with likelihood calculations. In the existing literature, these problems have mainly been addressed in the linear case. One of the main contributions of this paper is developing a method for the nonlinear case. Our approach is based on a combination of methods including Gibbs sampling, random permutation sampling, birth-death MCMC, and Kullback\u2013Leibler distance.<\/jats:p>","DOI":"10.1142\/s0219720008003710","type":"journal-article","created":{"date-parts":[[2008,9,2]],"date-time":"2008-09-02T11:01:05Z","timestamp":1220353265000},"page":"727-746","source":"Crossref","is-referenced-by-count":5,"title":["KULLBACK\u2013LEIBLER MARKOV CHAIN MONTE CARLO \u2014 A NEW ALGORITHM FOR FINITE MIXTURE ANALYSIS AND ITS APPLICATION TO GENE EXPRESSION DATA"],"prefix":"10.1142","volume":"06","author":[{"given":"TATIANA","family":"TATARINOVA","sequence":"first","affiliation":[{"name":"Department of Mathematics, Loyola Marymount University, Los Angeles, CA 90045, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JOHN","family":"BOUCK","sequence":"additional","affiliation":[{"name":"Ceres, Inc., 1535 Rancho Conejo Road, Thousand Oaks, CA 91320, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ALAN","family":"SCHUMITZKY","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Southern California, Los Angeles, CA 90089, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"volume-title":"A generic approach to posterior integration and Gibbs sampling, Technical Report","year":"1990","author":"M\u00fcller P.","key":"rf1"},{"volume-title":"Bayesian analysis of linear and nonlinear mixture models, Doctoral dissertation","year":"2006","author":"Tatarinova T.","key":"rf2"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2000.10474285"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1198\/016214501750333063"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1016120364"},{"key":"rf6","first-page":"768","volume":"59","author":"Stephens M.","journal-title":"J. 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