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arXiv 1702.07441math.PR
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几何性渐近可逆马尔可夫链的近似

Approximations of Geometrically Ergodic Reversible Markov Chains

Jeffrey Negrea, Jeffrey S. Rosenthal

AI总结:

本文提出基于希尔伯特空间$ L_2(π) $的工具,用于评估几何性渐近可逆马尔可夫链近似的准确性和效率,扩展了Johndrow等人[18]的结果,并应用于多种近似MCMC算法。

摘要:

马尔可夫链蒙特卡洛实践中,常用近似转移核来加速计算,当所需核缓慢评估或不可行时。现有用于评估此类近似相对准确性和效率的量化工具有限。我们基于旨在采样的稳态分布生成的希尔伯特空间$ L_2(π) $,推导了一组用于此类分析的工具。我们的结果适用于几何性渐近的可逆链的近似,这通常是马尔可夫链蒙特卡洛应用中常见的状况。我们的研究重点在于确定近似核是否会保持精确链的几何性渐近性,以及近似稳态分布是否接近原始稳态分布。对于可逆链,我们的结果在某些额外的正则性条件下,将Johndrow等人[18]关于均匀渐近情况的结果扩展到了几何性渐近情况。我们随后将这些结果应用于多种近似MCMC算法。

英文摘要:

A common tool in the practice of Markov Chain Monte Carlo is to use approximating transition kernels to speed up computation when the desired kernel is slow to evaluate or intractable. A limited set of quantitative tools exist to assess the relative accuracy and efficiency of such approximations. We derive a set of tools for such analysis based on the Hilbert space generated by the stationary distribution we intend to sample, $L_2(π)$. Our results apply to approximations of reversible chains which are geometrically ergodic, as is typically the case for applications to Markov Chain Monte Carlo. The focus of our work is on determining whether the approximating kernel will preserve the geometric ergodicity of the exact chain, and whether the approximating stationary distribution will be close to the original stationary distribution. For reversible chains, our results extend the results of Johndrow et al. [18] from the uniformly ergodic case to the geometrically ergodic case, under some additional regularity conditions. We then apply our results to a number of approximate MCMC algorithms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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