Adaptive independent Metropolis--Hastings

TL;DR

This paper introduces an adaptive independent Metropolis--Hastings algorithm that learns from previous proposals to efficiently generate samples from complex distributions, with proven convergence under certain tail conditions.

Contribution

It extends the independent Metropolis--Hastings algorithm by incorporating adaptation based on all past proposals, improving sampling efficiency.

Findings

Proves convergence under strong Doeblin condition.

Ensures exact sampling within finite iterations with high probability.

Applicable to Bayesian estimation and computationally intensive inverse problems.

Abstract

We propose an adaptive independent Metropolis--Hastings algorithm with the ability to learn from all previous proposals in the chain except the current location. It is an extension of the independent Metropolis--Hastings algorithm. Convergence is proved provided a strong Doeblin condition is satisfied, which essentially requires that all the proposal functions have uniformly heavier tails than the stationary distribution. The proof also holds if proposals depending on the current state are used intermittently, provided the information from these iterations is not used for adaption. The algorithm gives samples from the exact distribution within a finite number of iterations with probability arbitrarily close to 1. The algorithm is particularly useful when a large number of samples from the same distribution is necessary, like in Bayesian estimation, and in CPU intensive applications like, for example, in inverse problems and optimization.