Parallel Markov Chain Monte Carlo computation for varying-dimension signal analysis
Parallel Markov Chain Monte Carlo computation for varying-dimension signal analysis
复制标题
用于变维信号分析的并行马尔可夫链蒙特卡罗计算
DOI：
10.5281/zenodo.41499
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
J. Thompson
中科院分区：
文献类型：
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作者：
Jing Ye;A. Wallace;J. Thompson
马尔可夫链蒙特卡罗（MCMC）算法的贝叶斯推理的并行实现是有效的，但通常限于的情况下，其中的参数向量的维数是固定的。我们提出了一个有效的并行解决方案的变维问题，通过构建多个模型内MCMC链，然后结合单独的结果来分析后验分布的维度。我们的目标是通过减少老化期间的长度和单个链相比，串行，可逆跳MCMC（RJMCMC）算法的并行加速。平行的方法与应用程序的基准，变化点的问题。
Parallel implementation of Markov Chain Monte Carlo (MCMC) algorithms for Bayesian inference has been effective but is usually restricted to the case where the dimension of the parameter vector is fixed. We propose an efficient parallel solution for the varying-dimension problem by constructing multiple within-model MCMC chains and then combining the separate results to analyze the posterior distribution of dimensionality. We aim for parallel speed-up by reducing the length of the burn-in period and the individual chains in comparison with a serial, reversible jump MCMC (RJMCMC) algorithm. The parallel methodology is illustrated with application to a benchmarking, change point problem.