GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
复制标题
GPS-ABC：高斯过程代理近似贝叶斯计算
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Welling
中科院分区：
文献类型：
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作者：
Edward Meeds;M. Welling
科学家们经常通过计算要求很高的模拟程序来表达他们对世界的理解。分析给定观测值的参数的后验分布（逆问题）可能极具挑战性。近似贝叶斯计算（ABC）框架是处理这些无可能性问题的标准统计工具，但它们需要大量的模拟。在这项工作中，我们开发了两个新的ABC抽样算法，显着减少后验推理所需的模拟的数量。这两种算法都使用置信度估计的接受概率在大都会黑斯廷斯步骤自适应地选择必要的模拟的数量。我们的GPS-ABC算法存储从每个模拟中获得的信息在一个高斯过程中作为模拟统计的替代函数。一个具有挑战性的现实生物问题的实验说明了这些算法的潜力。
Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging. The Approximate Bayesian Computation (ABC) framework is the standard statistical tool to handle these likelihood free problems, but they require a very large number of simulations. In this work we develop two new ABC sampling algorithms that significantly reduce the number of simulations necessary for posterior inference. Both algorithms use confidence estimates for the accept probability in the Metropolis Hastings step to adaptively choose the number of necessary simulations. Our GPS-ABC algorithm stores the information obtained from every simulation in a Gaussian process which acts as a surrogate function for the simulated statistics. Experiments on a challenging realistic biological problem illustrate the potential of these algorithms.
影响因子：
2.7
作者：
Beaumont, Mark A.;Cornuet, Jean-Marie;Robert, Christian P.
通讯作者：
Robert, Christian P.
影响因子：
2.7
作者：
Conti, S.;Gosling, J. P.;O'Hagan, A.
通讯作者：
O'Hagan, A.