Efficient Monte Carlo simulation via the generalized splitting method-Reference-Cited by-同舟云学术

Efficient Monte Carlo simulation via the generalized splitting method

Published:2010-09-16 Issue:1 Volume:22 Page:1-16
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Botev Zdravko I.,Kroese Dirk P.

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s11222-010-9201-4.pdf

Reference35 articles.

1. Botev, Z.I.: Three examples of a practical exact Markov chain sampling. Postgraduate Seminar Paper, School of Mathematics and Physics, The University of Queensland. http://espace.library.uq.edu.au/view/UQ:130865 (2007)

2. Botev, Z.I.: An algorithm for rare-event probability estimation using the product rule of probability theory. Technical report, School of Mathematics and Physics, The University of Queensland. http://espace.library.uq.edu.au/view/UQ:151299 (2008)

3. Botev, Z.I.: Splitting methods for efficient combinatorial counting and rare-event probability estimation. Technical report, School of Mathematics and Physics, The University of Queensland. http://espace.library.uq.edu.au/view/UQ:178513 (2009)

4. Botev, Z.I., Kroese, D.P.: The generalized cross entropy method, with applications to probability density estimation. Methodol. Comput. Appl. Probab. (2009). doi: 10.1007/s11009-009-9133-7

5. Brooks, S.P., Roberts, G.O.: Convergence assessment techniques for Markov Chain Monte Carlo. Stat. Comput. 8, 319–335 (1998)

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