Comment on "Sequential Monte Carlo for Bayesian Computation" (P. Del Moral, A. Doucet, A. Jasra)

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dc.creatorBickel, David R.
dc.date2006-06-22
dc.date.accessioned2026-07-07T08:07:58Z
dc.date.available2026-07-07T08:07:58Z
dc.descriptionThe main question concerns another recent advance in sequential Monte Carlo, the use of a mixture transition kernel that automatically adapts to the target distribution (Douc et al. 2006). Is there a class of static inference problems for which the backward-kernel approach is better suited, or is it too early to predict which method may have better performance in a particular situation?
dc.descriptionTo appear in the published proceedings of the Eighth Valencia International Meeting on Bayesian Statistics
dc.identifierhttps://arxiv.org/abs/math/0606557
dc.identifierhttp://arxiv.org/abs/math/0606557
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131106
dc.subjectStatistics Theory
dc.subjectProbability
dc.titleComment on "Sequential Monte Carlo for Bayesian Computation" (P. Del Moral, A. Doucet, A. Jasra)
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