AABC: approximate approximate Bayesian computation for inference in population-genetic models.
AABC: approximate approximate Bayesian computation for inference in population-genetic models.
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AABC：大约近似贝叶斯计算，用于推断人口基因模型的推断。
DOI：
10.1016/j.tpb.2014.09.002
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发表时间：
2015-02
影响因子：
1.4
通讯作者：
Rosenberg, Noah A.
中科院分区：
文献类型：
--
作者：
Buzbas, Erkan O.;Rosenberg, Noah A.
关键词：
近似贝叶斯计算（ABC）方法执行推理的模型特定的参数的机械动机的参数模型时，评估的可能性是困难的。ABC方法在生物学中经常使用，其成功的关键是从感兴趣的参数模型中对数据集进行计算上廉价的模拟。然而，当从模型中模拟数据集的计算成本如此之高，以至于参数的后验分布无法通过ABC充分采样时，推理并不简单。我们提出了“近似近似贝叶斯计算”（AABC），一类计算快速推理方法，扩展ABC模型，模拟数据是昂贵的。在AABC中，我们首先模拟了一些足够小的数据集，这些数据集在计算上是可行的，可以从参数模型中进行模拟。在这些数据集的条件下，我们使用一个统计模型，近似正确的参数模型，并能够有效地模拟大量的数据集。我们表明，在温和的假设下，AABC得到的后验分布收敛到ABC得到的后验分布，从参数模型模拟的数据集的数量和观察到的数据集的样本量增加。我们证明了AABC的性能上的人口遗传模型的自然选择，以及混合历史的混合动力种群的模型。后一个例子说明了在群体遗传学中，AABC在依赖于概念上简单但可能缓慢的时间向前模拟的场景中特别有用。
Approximate Bayesian computation (ABC) methods perform inference on model-specific parameters of mechanistically motivated parametric models when evaluating likelihoods is difficult. Central to the success of ABC methods, which have been used frequently in biology, is computationally inexpensive simulation of data sets from the parametric model of interest. However, when simulating data sets from a model is so computationally expensive that the posterior distribution of parameters cannot be adequately sampled by ABC, inference is not straightforward. We present “approximate approximate Bayesian computation” (AABC), a class of computationally fast inference methods that extends ABC to models in which simulating data is expensive. In AABC, we first simulate a number of data sets small enough to be computationally feasible to simulate from the parametric model. Conditional on these data sets, we use a statistical model that approximates the correct parametric model and enables efficient simulation of a large number of data sets. We show that under mild assumptions, the posterior distribution obtained by AABC converges to the posterior distribution obtained by ABC, as the number of data sets simulated from the parametric model and the sample size of the observed data set increase. We demonstrate the performance of AABC on a population-genetic model of natural selection, as well as on a model of the admixture history of hybrid populations. This latter example illustrates how, in population genetics, AABC is of particular utility in scenarios that rely on conceptually straightforward but potentially slow forward-in-time simulations.
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影响因子：
1.7
作者：
Joyce, Paul;Genz, Alan;Buzbas, Erkan Ozge
通讯作者：
Buzbas, Erkan Ozge
DOI：
10.2202/1544-6115.1576
发表时间：
2010-01-01
影响因子：
0.9
作者：
Nunes, Matthew A.;Balding, David J.
通讯作者：
Balding, David J.
影响因子：
4.5
作者：
François O;Blum MG;Jakobsson M;Rosenberg NA
通讯作者：
Rosenberg NA
DOI：
10.2202/1544-6115.1684
发表时间：
2011-01-01
影响因子：
0.9
作者：
Bonassi, Fernando V.;You, Lingchong;West, Mike
通讯作者：
West, Mike
影响因子：
4.4
作者：
Grelaud, Aude;Robert, Christian P.;Taly, Jean-Francois
通讯作者：
Taly, Jean-Francois