Stochastic adaptation of importance sampler
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...
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sg-ntu-dr.10356-957242020-03-07T12:37:20Z Stochastic adaptation of importance sampler Lian, Heng School of Physical and Mathematical Sciences 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. 2013-07-23T01:49:33Z 2019-12-06T19:20:22Z 2013-07-23T01:49:33Z 2019-12-06T19:20:22Z 2012 2012 Journal Article Lian, H. (2012). Stochastic adaptation of importance sampler. Statistics, 46(6), 777-785. https://hdl.handle.net/10356/95724 http://hdl.handle.net/10220/11998 10.1080/02331888.2011.555549 en Statistics © 2012 Taylor & Francis. |
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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. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Lian, Heng |
| format |
Article |
| author |
Lian, Heng |
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Lian, Heng Stochastic adaptation of importance sampler |
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Lian, Heng |
| title |
Stochastic adaptation of importance sampler |
| title_short |
Stochastic adaptation of importance sampler |
| title_full |
Stochastic adaptation of importance sampler |
| title_fullStr |
Stochastic adaptation of importance sampler |
| title_full_unstemmed |
Stochastic adaptation of importance sampler |
| title_sort |
stochastic adaptation of importance sampler |
| publishDate |
2013 |
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https://hdl.handle.net/10356/95724 http://hdl.handle.net/10220/11998 |
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