Adaptive independent Metropolis--Hastings

Details Adaptive independent Metropolis--Hastings Adaptive independent Metropolis--Hastings

2009-03-03

arxiv.org/abs/0903.0483v1

Annals of Applied Probability 2009, Vol. 19, No. 1, 395-413

Mathematics

Probability

Published in at http://dx.doi.org/10.1214/08-AAP545 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Inst

Scientific paper

10.1214/08-AAP545

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.

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