{"paper":{"title":"Model selection of stochastic simulation algorithm based on generalized divergence measures","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Badiassiatta Don Bosco Diatta, Papa Ngom","submitted_at":"2014-01-20T18:59:01Z","abstract_excerpt":"MCMC methods (Monte Carlo Markov Chain) are a class of methods used to perform simulations per a probability distribution $P$. These methods are often used when we have difficulties to directly sample per a given probability distribution $P$ . This distribution is then considered as a target and generates a Markov chain $(X_n)_{n\\in\\mathbb{N}}$ that, when $n$ is large we have $X_n\\sim P$. These MCMC methods consist of several simulation strategies including the \\emph{Independent Sampler (IS)}, the \\emph{Random Walk of Metropolis Hastings \\small{(RWMH)}}, the \\emph{Gibbs sampler}, the \\emph{Ada"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1401.5015","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}