Efficient Bayes factor estimation from the reversible jump output
Efficient Bayes factor estimation from the reversible jump output
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DOI：
10.1093/biomet/93.1.41
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发表时间：
2006-03-01
期刊：
影响因子：
2.7
通讯作者：
Mira, A
中科院分区：
文献类型：
--
作者：
Bartolucci, F;Scaccia, L;Mira, A
本文提出了一类Bayes因子的估计量，它是基于Meng & Wong（1996）的桥抽样恒等式的推广，并利用了绿色（1995）的可逆跳算法的输出。在这个类中，我们给出了最优估计，也可以简单地计算的基础上使用的可逆跳跃算法模型之间的跳跃的接受概率的次优之一。所提出的估计量是非常容易计算，并导致在估计贝叶斯因子的效率大大提高了基于可逆跳输出的标准估计量。这是说明通过一系列的Monte Carlo模拟，涉及线性和逻辑回归模型。
We propose a class of estimators of the Bayes factor which is based on an extension of the bridge sampling identity of Meng & Wong (1996) and makes use of the output of the reversible jump algorithm of Green (1995). Within this class we give the optimal estimator and also a suboptimal one which may be simply computed on the basis of the acceptance probabilities used within the reversible jump algorithm for jumping between models. The proposed estimators are very easily computed and lead to a substantial gain of efficiency in estimating the Bayes factor over the standard estimator based on the reversible jump output. This is illustrated through a series of Monte Carlo simulations involving a linear and a logistic regression model.