A weighted discrepancy bound of quasi-Monte Carlo importance sampling

Josef Dick; Daniel Rudolf; Houying Zhu

doi:10.1016/j.spl.2019.01.014

A weighted discrepancy bound of quasi-Monte Carlo importance sampling

Josef Dick, Daniel Rudolf, Houying Zhu

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Importance sampling Monte-Carlo methods are widely used for the approximation of expectations with respect to partially known probability measures. In this paper we study a deterministic version of such an estimator based on quasi-Monte Carlo. We obtain an explicit error bound in terms of the star-discrepancy for this method.

Original language	English
Pages (from-to)	100-106
Number of pages	7
Journal	Statistics and Probability Letters
Volume	149
DOIs	https://doi.org/10.1016/j.spl.2019.01.014
Publication status	Published - Jun 2019
Externally published	Yes

Keywords

Importance sampling
Monte Carlo method
Quasi-Monte Carlo

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10.1016/j.spl.2019.01.014

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title = "A weighted discrepancy bound of quasi-Monte Carlo importance sampling",

abstract = "Importance sampling Monte-Carlo methods are widely used for the approximation of expectations with respect to partially known probability measures. In this paper we study a deterministic version of such an estimator based on quasi-Monte Carlo. We obtain an explicit error bound in terms of the star-discrepancy for this method.",

keywords = "Importance sampling, Monte Carlo method, Quasi-Monte Carlo",

author = "Josef Dick and Daniel Rudolf and Houying Zhu",

year = "2019",

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doi = "10.1016/j.spl.2019.01.014",

language = "English",

volume = "149",

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T1 - A weighted discrepancy bound of quasi-Monte Carlo importance sampling

AU - Dick, Josef

AU - Rudolf, Daniel

AU - Zhu, Houying

PY - 2019/6

Y1 - 2019/6

N2 - Importance sampling Monte-Carlo methods are widely used for the approximation of expectations with respect to partially known probability measures. In this paper we study a deterministic version of such an estimator based on quasi-Monte Carlo. We obtain an explicit error bound in terms of the star-discrepancy for this method.

AB - Importance sampling Monte-Carlo methods are widely used for the approximation of expectations with respect to partially known probability measures. In this paper we study a deterministic version of such an estimator based on quasi-Monte Carlo. We obtain an explicit error bound in terms of the star-discrepancy for this method.

KW - Importance sampling

KW - Monte Carlo method

KW - Quasi-Monte Carlo

UR - http://www.scopus.com/inward/record.url?scp=85061826767&partnerID=8YFLogxK

U2 - 10.1016/j.spl.2019.01.014

DO - 10.1016/j.spl.2019.01.014

M3 - Article

SN - 0167-7152

VL - 149

SP - 100

EP - 106

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

ER -

A weighted discrepancy bound of quasi-Monte Carlo importance sampling

Abstract

Keywords

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