Bayesian computation: a summary of the current state, and samples backwards and forwards
Bayesian computation: a summary of the current state, and samples backwards and forwards
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DOI：
10.1007/s11222-015-9574-5
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发表时间：
2015-07-01
影响因子：
2.2
通讯作者：
Robert, Christian P.
中科院分区：
文献类型：
--
作者：
Green, Peter J.;Latuszynski, Krzysztof;Robert, Christian P.
近几十年来，在统计模型的计算推断方面有了巨大的改进；在各种计算工具中有了竞争性的不断增强。在贝叶斯推理中，首先也是最重要的是，MCMC技术不断发展，从随机游走提议到朗之万漂，再到哈密尔顿蒙特卡罗等等，理论和算法创新都为实践者打开了新的机会。然而，这种令人印象深刻的容量演变面临着要处理的数据集的复杂性甚至更陡峭的增加。建模和处理越来越复杂的数据集的困难很可能需要一种新型的计算推理工具，这种工具可以显著降低原始数据的维度和大小，同时捕获其基本方面。因此，近似模型和算法可能成为下一次计算革命的核心。
Recent decades have seen enormous improvements in computational inference for statistical models; there have been competitive continual enhancements in a wide range of computational tools. In Bayesian inference, first and foremost, MCMC techniques have continued to evolve, moving from random walk proposals to Langevin drift, to Hamiltonian Monte Carlo, and so on, with both theoretical and algorithmic innovations opening new opportunities to practitioners. However, this impressive evolution in capacity is confronted by an even steeper increase in the complexity of the datasets to be addressed. The difficulties of modelling and then handling ever more complex datasets most likely call for a new type of tool for computational inference that dramatically reduces the dimension and size of the raw data while capturing its essential aspects. Approximate models and algorithms may thus be at the core of the next computational revolution.