Bayesian exoplanet tests of a new method for MCMC sampling in highly correlated model parameter spaces
Bayesian exoplanet tests of a new method for MCMC sampling in highly correlated model parameter spaces
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DOI：
10.1111/j.1365-2966.2010.17428.x
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发表时间：
2011
影响因子：
4.8
通讯作者：
P. Gregory
中科院分区：
文献类型：
--
作者：
P. Gregory
马尔可夫链蒙特卡罗（MCMC）方法是一种方便贝叶斯非线性模型拟合的强大技术。在许多情况下，参数空间的MCMC探索是非常低效的，因为模型参数高度相关。差分进化MCMC是一种通过采用多个并行链来解决这个问题的技术。我们提出了一种新的方法，自动实现高效的MCMC采样高度相关的参数空间，这不需要额外的链来实现这一点。它被设计为与现有的混合MCMC（HMCMC）算法，它结合了并行回火，模拟退火和遗传交叉操作。这些功能，连同新的相关参数采样器，极大地促进了检测的全局最小值在χ 2。新的HMCMC算法在范围上是非常通用的。利用（a）系外行星精确径向速度（RV）数据和（B）模拟空间天体测量数据描述了算法的两个测试。后一个测试探讨的准确性与贝叶斯HMCMC算法获得的参数估计的假设天体测量噪声。
The Markov chain Monte Carlo (MCMC) method is a powerful technique for facilitating Bayesian non-linear model fitting. In many cases, the MCMC exploration of the parameter space is very inefficient, because the model parameters are highly correlated. Differential evolution MCMC is one technique that addresses this problem by employing multiple parallel chains. We present a new method that automatically achieves efficient MCMC sampling in highly correlated parameter spaces, which does not require additional chains to accomplish this. It was designed to work with an existing hybrid MCMC (HMCMC) algorithm, which incorporates parallel tempering, simulated annealing and genetic cross-over operations. These features, together with the new correlated parameter sampler, greatly facilitate the detection of a global minimum in χ 2 . The new HMCMC algorithm is very general in scope. Two tests of the algorithm are described employing (a) exoplanet precision radial velocity (RV) data and (b) simulated space astrometry data. The latter test explores the accuracy of parameter estimates obtained with the Bayesian HMCMC algorithm on the assumed astrometric noise.