Bayesian Update with Importance Sampling: Required Sample Size

Autor:	Daniel Sanz-Alonso, Zijian Wang
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	importance sampling Bayesian update sample size singular limits particle filtering Science Astrophysics QB460-466 Physics QC1-999
Zdroj:	Entropy, Vol 23, Iss 1, p 22 (2020)
Druh dokumentu:	article
ISSN:	1099-4300
DOI:	10.3390/e23010022
Popis:	Importance sampling is used to approximate Bayes’ rule in many computational approaches to Bayesian inverse problems, data assimilation and machine learning. This paper reviews and further investigates the required sample size for importance sampling in terms of the χ2-divergence between target and proposal. We illustrate through examples the roles that dimension, noise-level and other model parameters play in approximating the Bayesian update with importance sampling. Our examples also facilitate a new direct comparison of standard and optimal proposals for particle filtering.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/90cea68ac4ac4c9fa732d1861716e85a Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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