Adaptive approximate Bayesian computation
Adaptive approximate Bayesian computation
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DOI：
10.1093/biomet/asp052
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发表时间：
2009-12-01
期刊：
影响因子：
2.7
通讯作者：
Robert, Christian P.
中科院分区：
文献类型：
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作者：
Beaumont, Mark A.;Cornuet, Jean-Marie;Robert, Christian P.
顺序技术可以提高效率的近似贝叶斯计算算法，如在Sisson等人。的（2007）部分拒绝控制版本。虽然这种方法是基于Del Moral等人（2006）的理论工作，但应用于近似贝叶斯计算会导致后验近似的偏差。基于真正重要性抽样参数的替代版本绕过了这个困难，与Cappe等人（2004）的人口蒙特卡罗方法有关，它包括前向内核的自动缩放。当应用于人口遗传学的例子，它相比，毫不逊色于其他两个版本的近似算法。
Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version. While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior. An alternative version based on genuine importance sampling arguments bypasses this difficulty, in connection with the population Monte Carlo method of Cappe et al. (2004), and it includes an automatic scaling of the forward kernel. When applied to a population genetics example, it compares favourably with two other versions of the approximate algorithm.