Stochastic adaptation of importance sampler
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
|Journal / Publication||Statistics|
|Publication status||Published - Dec 2012|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-84868107393&origin=recordpage|
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. © 2012 Copyright Taylor and Francis Group, LLC.
- adaptive algorithm, importance sampling, stochastic approximation