Monte Carlo Integration Using Importance Sampling and Gibbs Sampling

Hörmann, Wolfgang and Leydold, Josef (2005) Monte Carlo Integration Using Importance Sampling and Gibbs Sampling. Preprint Series / Department of Applied Statistics and Data Processing, 53. Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, Vienna.


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To evaluate the expectation of a simple function with respect to a complicated multivariate density Monte Carlo integration has become the main technique. Gibbs sampling and importance sampling are the most popular methods for this task. In this contribution we propose a new simple general purpose importance sampling procedure. In a simulation study we compare the performance of this method with the performance of Gibbs sampling and of importance sampling using a vector of independent variates. It turns out that the new procedure is much better than independent importance sampling; up to dimension five it is also better than Gibbs sampling. The simulation results indicate that for higher dimensions Gibbs sampling is superior. (author's abstract)

Item Type: Paper
Additional Information: published in: H. Dag and Y. Deng (eds.), Proceedings of the International Conference on Computational Science and Engineering, Istanbul, pp. 92-97, 2005
Keywords: Markov chain Monte Carlo method / Gibbs sampling / importance sampling
Classification Codes: MSC 65C05
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 10 Jul 2006 13:45
Last Modified: 22 Oct 2019 00:41


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