Self-regenerative Markov chain Monte Carlo with adaptation. (English) Zbl 1044.62033

Summary: A new method of construction of Markov chains with a given stationary distribution is proposed. The method is based on constructing an auxiliary chain with some other stationary distribution and picking elements of this auxiliary chain a suitable number of times. The proposed method is easy to implement and analyse; it may be more efficient than other related Markov chain Monte Carlo techniques. The main attractive feature of the associated Markov chain is that it regenerates whenever it accepts a new proposed point. This makes the algorithm easy to adapt and tune for practical problems. A theoretical study and numerical comparisons with some other available Markov chain Monte Carlo techniques are presented.


62F15 Bayesian inference
65C40 Numerical analysis or methods applied to Markov chains
60J22 Computational methods in Markov chains
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