A method for importance sampling through Markov chain Monte Carlo with post sampling variational estimate
Computation
2013-12-10 v1
Abstract
We propose a method to efficiently integrate truncated probability densities. The method uses Markov chain Monte Carlo method to sample from a probability density matching the function being integrated. The required normalisation or equivalently the result is obtained by constructing a function with known integral, through non-parametric kernel density estimation and variational procedure. The method is demonstrated with numerical case studies. Possible enhancements to the method and limitations are discussed.
Cite
@article{arxiv.1312.2556,
title = {A method for importance sampling through Markov chain Monte Carlo with post sampling variational estimate},
author = {A. John Arul and Kannan Iyer},
journal= {arXiv preprint arXiv:1312.2556},
year = {2013}
}