Langevin type limiting processes for adaptive MCMC
Article Type
Research Article
Publication Title
Indian Journal of Pure and Applied Mathematics
Abstract
Adaptive Markov Chain Monte Carlo (AMCMC) is a class of MCMC algorithms where the proposal distribution changes at every iteration of the chain. In this case it is important to verify that such a Markov Chain indeed has a stationary distribution. In this paper we discuss a diffusion approximation to a discrete time AMCMC. This diffusion approximation is different when compared to the diffusion approximation as in Gelman et al. [5] where the state space increases in dimension to ∞. In our approach the time parameter is sped up in such a way that the limiting process (as the mesh size goes to 0) approaches to a non-trivial diffusion process.
First Page
301
Last Page
328
DOI
10.1007/s13226-016-0189-0
Publication Date
6-1-2016
Recommended Citation
Basak, G. K. and Biswas, Arunangshu, "Langevin type limiting processes for adaptive MCMC" (2016). Journal Articles. 4273.
https://digitalcommons.isical.ac.in/journal-articles/4273
