{ "id": "1108.3201", "version": "v2", "published": "2011-08-16T10:30:31.000Z", "updated": "2013-11-16T23:30:54.000Z", "title": "Explicit error bounds for Markov chain Monte Carlo", "authors": [ "Daniel Rudolf" ], "journal": "Dissertationes Math. 485 (2012), 93 pp", "categories": [ "math.PR", "math.NA" ], "abstract": "We prove explicit, i.e. non-asymptotic, error bounds for Markov chain Monte Carlo methods. The problem is to compute the expectation of a function f with respect to a measure {\\pi}. Different convergence properties of Markov chains imply different error bounds. For uniformly ergodic and reversible Markov chains we prove a lower and an upper error bound with respect to the L2 -norm of f . If there exists an L2 -spectral gap, which is a weaker convergence property than uniform ergodicity, then we show an upper error bound with respect to the Lp -norm of f for p > 2. Usually a burn-in period is an efficient way to tune the algorithm. We provide and justify a recipe how to choose the burn-in period. The error bounds are applied to the problem of the integration with respect to a possibly unnormalized density. More precise, we consider the integration with respect to log-concave densities and the integration over convex bodies. By the use of the Metropolis algorithm based on a ball walk and the hit-and-run algorithm it is shown that both problems are polynomial tractable.", "revisions": [ { "version": "v2", "updated": "2013-11-16T23:30:54.000Z" } ], "analyses": { "subjects": [ "65C05", "60J10", "60J05", "60J22", "65C20", "37A25", "62F25", "65Y20" ], "keywords": [ "explicit error bounds", "upper error bound", "markov chain monte carlo methods", "convergence property", "burn-in period" ], "tags": [ "journal article" ], "publication": { "journal": "Ph.D. Thesis", "year": 2011 }, "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable", "adsabs": "2011PhDT.......182R" } } }