Approximate Bayesian computational methods
Approximate Bayesian computational methods
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DOI：
10.1007/s11222-011-9288-2
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发表时间：
2012-11-01
影响因子：
2.2
通讯作者：
Ryder, Robin J.
中科院分区：
文献类型：
--
作者：
Marin, Jean-Michel;Pudlo, Pierre;Ryder, Robin J.
近似贝叶斯计算（ABC）方法，也被称为无似然技术，在过去的十年中出现了最令人满意的方法来解决棘手的可能性问题，首先在遗传学中，然后在更广泛的应用。然而，这些方法在一定程度上遭受校准困难，使他们在其实施中相当不稳定，从而使他们怀疑更传统的蒙特卡罗方法的用户。在这项调查中，我们研究了近年来对原始ABC算法的各种改进和扩展。
Approximate Bayesian Computation (ABC) methods, also known as likelihood-free techniques, have appeared in the past ten years as the most satisfactory approach to intractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from calibration difficulties that make them rather volatile in their implementation and thus render them suspicious to the users of more traditional Monte Carlo methods. In this survey, we study the various improvements and extensions brought on the original ABC algorithm in recent years.