Towards optimal scaling of metropolis-coupled Markov chain Monte Carlo
نویسندگان
چکیده
We consider optimal temperature spacings for Metropolis-coupled Markov chain Monte Carlo (MCMCMC) and Simulated Tempering algorithms. We prove that, under certain conditions, it is optimal (in terms of maximising the expected squared jumping distance) to space the temperatures so that the proportion of temperature swaps which are accepted is approximately 0.234. This generalises related work by physicists, and is consistent with previous work about optimal scaling of random-walk Metropolis algorithms.
منابع مشابه
Optimal Scaling of Metropolis-coupled Markov Chain Monte Carlo
We consider optimal temperature spacings for Metropolis-coupled Markov chain Monte Carlo (MCMCMC) and Simulated Tempering algorithms. We prove that, under certain conditions, it is optimal to space the temperatures so that the proportion of temperature swaps which are accepted is approximately 0.234. This generalises related work by physicists, and is consistent with previous work about optimal...
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ورودعنوان ژورنال:
- Statistics and Computing
دوره 21 شماره
صفحات -
تاریخ انتشار 2011