A central limit theorem for adaptive and interacting Markov chains
The paper studies a general (non-Markovian) simulation framework covering both the adaptive and interacting Markov chains Monte Carlo (MCMC) algorithms. In Section 2, it contains the main theorem which establishes central limit theorems for adaptive and interacting MCMC algorithms. These results are applied in Section 3.2 to the 2-chain interacting tempering algorithm which is a simplified version of the equi-energy sampler.
interacting Markov chain Monte Carlo algorithms
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