Marginal likelihood estimation via power posteriors
Marginal likelihood estimation via power posteriors
复制标题
DOI：
10.1111/j.1467-9868.2007.00650.x
复制
发表时间：
2008-01-01
影响因子：
5.8
通讯作者：
Pettitt, A. N.
中科院分区：
文献类型：
--
作者：
Friel, N.;Pettitt, A. N.
Model choice plays an increasingly important role in statistics. From a Bayesian perspective a crucial goal is to compute the marginal likelihood of the data for a given model. However, this is typically a difficult task since it amounts to integrating over all model parameters. The aim of the paper is to illustrate how this may be achieved by using ideas from thermodynamic integration or path sampling. We show how the marginal likelihood can be computed via Markov chain Monte Carlo methods on modified posterior distributions for each model. This then allows Bayes factors or posterior model probabilities to be calculated. We show that this approach requires very little tuning and is straightforward to implement. The new method is illustrated in a variety of challenging statistical settings.
