Adaptive independent sticky MCMC algorithms

Adaptive independent sticky MCMC algorithms

Monte Carlo methods have become essential tools to solve complex Bayesian inference problems in different fields, such as computational statistics, machine learning, and statistical signal processing. In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky Markov Chain Monte Carlo (MCMC) algorithms, to sample efficiently from any bounded targ...

Limit theorems for some adaptive MCMC algorithms with subgeometric kernels

Abstract. This paper deals with the ergodicity (convergence of the marginals) and the law of large numbers for adaptive MCMC algorithms built from transition kernels that are not necessarily geometrically ergodic. We develop a number of results that broaden significantly the class of adaptive MCMC algorithms for which rigorous analysis is now possible. As an example, we give a detailed analysis...

Some Results on the Ergodicity of Adaptive MCMC Algorithms

On the e ciency of adaptive MCMC algorithms

Abstract We study a class of adaptive Markov Chain Monte Carlo (MCMC) processes which aim at behaving as an optimal target process via a learning procedure. We show, under appropriate conditions, that the adaptive process and the optimal (nonadaptive) MCMC process share identical asymptotic properties. The special case of adaptive MCMC algorithms governed by stochastic approximation is consider...

DRAM: Efficient adaptive MCMC

We propose to combine two quite powerful ideas that have recently appeared in the Markov chain Monte Carlo literature: adaptive Metropolis samplers and delayed rejection. The ergodicity of the resulting non–Markovian sampler is proved, and the efficiency of the combination is demonstrated with various examples. We present situations where the combination outperforms the original methods: adapta...