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Zero variance in Markov chain Monte Carlo with an application to credit risk estimation [PDF]

We propose a general purpose variance reduction technique for Markov Chain Monte Carlo estimators based on the Zero-Variance principle introduced in the physics lit- erature by Assaraf and Caarel ( 1999). The potential of the new idea is illustrated with
Tenconi Paolo
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Enhancing quasi-Monte Carlo methods via delta dimension and directional control technique

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Reverse Monte Carlo Simulation: A New Technique for the Determination of Disordered Structures

Molecular Simulation, 1988
R L McGreevy
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Summarizing Monte Carlo Results in Methodological Research: The One- and Two-Factor Fixed Effects ANOVA Cases

Journal of Educational Statistics, 1992
Michael Harwell
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Exchange Monte Carlo Method and Application to Spin Glass Simulations

Journal of the Physical Society of Japan, 1996
Koji Hukushima
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Particle Markov Chain Monte Carlo Methods

Journal of the Royal Statistical Society Series B: Statistical Methodology, 2010
Arnaud Doucet, Christophe Andrieu
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Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications

Reliability Engineering and System Safety, 2019
Tianfeng Hou, Staf Roels, Dirk Nuyens
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Kernel-predicting convolutional networks for denoising Monte Carlo renderings

ACM Transactions on Graphics, 2017
Thijs Vogels, Pradeep Sen, Jan Novak
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Monte Carlo methods for security pricing

Journal of Economic Dynamics and Control, 1997
Paul Glasserman, Mark Broadie, Phelim Boyle   +2 more
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Monte Carlo simulations in zeolites

Current Opinion in Solid State and Materials Science, 2001
Rajamani Krishna, Berend Smit
exaly