Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/95724
Title: Stochastic adaptation of importance sampler
Authors: Lian, Heng
Issue Date: 2012
Source: Lian, H. (2012). Stochastic adaptation of importance sampler. Statistics, 46(6), 777-785.
Series/Report no.: Statistics
Abstract: Improving efficiency of the importance sampler is at the centre of research on Monte Carlo methods. While the adaptive approach is usually not so straightforward within the Markov chain Monte Carlo framework, the counterpart in importance sampling can be justified and validated easily. We propose an iterative adaptation method for learning the proposal distribution of an importance sampler based on stochastic approximation. The stochastic approximation method can recruit general iterative optimization techniques like the minorization–maximization algorithm. The effectiveness of the approach in optimizing the Kullback divergence between the proposal distribution and the target is demonstrated using several examples.
URI: https://hdl.handle.net/10356/95724
http://hdl.handle.net/10220/11998
DOI: http://dx.doi.org/10.1080/02331888.2011.555549
Rights: © 2012 Taylor & Francis.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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