Bayesian Computation and Model Selection Without Likelihoods
Bayesian Computation and Model Selection Without Likelihoods
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DOI：
10.1534/genetics.109.109058
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发表时间：
2010-01-01
期刊：
影响因子：
3.3
通讯作者：
Wegmann, Daniel
中科院分区：
文献类型：
--
作者：
Leuenberger, Christoph;Wegmann, Daniel
直到最近，贝叶斯推理的使用还仅限于少数情况，因为对于许多现实的概率模型来说，似然函数无法通过分析计算。随着无似然推理算法的出现，这种情况发生了变化，通常被归入术语近似贝叶斯计算（ABC）之下。一项关键的创新是使用后采样回归调整，允许更大的容差值，从而将计算时间转移到实际的数量级。在这里，我们建议根据一般线性模型（GLM）重新制定回归调整。这使得可以集成到贝叶斯统计的合理理论框架及其方法的使用中，包括通过贝叶斯因子进行模型选择。然后，我们将所提出的方法应用于西方黑猩猩（Pan troglodytes verus）的种群细分问题。
Until recently, the use of Bayesian inference was limited to a few cases because for many realistic probability models the likelihood function cannot be calculated analytically. The situation changed with the advent of likelihood-free inference algorithms, often subsumed under the term approximate Bayesian computation (ABC). A key innovation was the use of a postsampling regression adjustment, allowing larger tolerance values and as such shifting computation time to realistic orders of magnitude. Here we propose a reformulation of the regression adjustment in terms of a general linear model (GLM). This allows the integration into the sound theoretical framework of Bayesian statistics and the use of its methods, including model selection via Bayes factors. We then apply the proposed methodology to the question of population subdivision among western chimpanzees, Pan troglodytes verus.