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arXiv · 1511.08100

Eigenvalue analysis of an irreversible random walk with skew detailed balance conditions

Yuji Sakai·Koji Hukushima
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Abstract

An irreversible Markov-chain Monte Carlo (MCMC) algorithm with skew detailed balance conditions originally proposed by Turitsyn et al. is extended to general discrete systems on the basis of the Metropolis-Hastings scheme. To evaluate the efficiency of our proposed method, the relaxation dynamics of the slowest mode and the asymptotic variance are studied analytically in a random walk on one dimension. It is found that the performance in irreversible MCMC methods violating the detailed balance condition is improved by appropriately choosing parameters in the algorithm.

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Yuji Sakai, Koji Hukushima. 2015-11-25. Eigenvalue analysis of an irreversible random walk with skew detailed balance conditions. https://doi.org/10.1103/physreve.93.043318

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