Sampling using a `bank' of clues

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dc.creatorAllanach, Benjamin C.
dc.creatorLester, Christopher G.
dc.date2007-05-03
dc.date2007-11-26
dc.date.accessioned2026-07-07T11:41:05Z
dc.date.available2026-07-07T11:41:05Z
dc.descriptionAn easy-to-implement form of the Metropolis Algorithm is described which, unlike most standard techniques, is well suited to sampling from multi-modal distributions on spaces with moderate numbers of dimensions (order ten) in environments typical of investigations into current constraints on Beyond-the-Standard-Model physics. The sampling technique makes use of pre-existing information (which can safely be of low or uncertain quality) relating to the distribution from which it is desired to sample. This information should come in the form of a ``bank'' or ``cache'' of space points of which at least some may be expected to be near regions of interest in the desired distribution. In practical circumstances such ``banks of clues'' are easy to assemble from earlier work, aborted runs, discarded burn-in samples from failed sampling attempts, or from prior scouting investigations. The technique equilibrates between disconnected parts of the distribution without user input. The algorithm is not lead astray by ``bad'' clues, but there is no free lunch: performance gains will only be seen where clues are helpful.
dc.descriptionv1: 18 pages, 7 figures. v2: 22 pages, 9 figures: no changes to the algorithm, but more example distributions are provided against which the sampler is tested
dc.identifierhttps://arxiv.org/abs/0705.0486
dc.identifierhttp://arxiv.org/abs/0705.0486
dc.identifierComput.Phys.Commun.179:256-266,2008
dc.identifierdoi:10.1016/j.cpc.2008.02.020
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/200411
dc.subjectHigh Energy Physics - Phenomenology
dc.subjectData Analysis, Statistics and Probability
dc.titleSampling using a `bank' of clues
dc.typetext

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