Bayesian Nonparametric Inference - Why and How.
Bayesian Nonparametric Inference - Why and How.
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DOI：
10.1214/13-ba811
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发表时间：
2013
影响因子：
4.4
通讯作者：
Mitra R
中科院分区：
文献类型：
--
作者：
Müller P;Mitra R
我们回顾了非参数贝叶斯（BNP）先验模型下的推断。讨论遵循一些常见的推理问题的一组例子。选择的例子突出的问题是具有挑战性的标准参数推断。我们讨论了密度估计，聚类，回归和随机效应分布的混合效应模型的推断。虽然我们专注于论证BNP模型的灵活性，但我们也回顾了一些更常用的BNP模型，因此希望能回答这两个问题，为什么以及如何使用BNP。
We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference. We discuss inference for density estimation, clustering, regression and for mixed effects models with random effects distributions. While we focus on arguing for the need for the flexibility of BNP models, we also review some of the more commonly used BNP models, thus hopefully answering a bit of both questions, why and how to use BNP.