Müller Peter, Mitra Riten
Department of Mathematics, University of Texas,
ICES, University of Texas,
Bayesian Anal. 2013;8(2). doi: 10.1214/13-BA811.
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.
我们回顾了具有非参数贝叶斯（BNP）先验的模型下的推断。讨论围绕一些常见推断问题的一系列示例展开。选择这些示例是为了突出对标准参数推断具有挑战性的问题。我们讨论了密度估计、聚类、回归以及具有随机效应分布的混合效应模型的推断。虽然我们着重论证BNP模型灵活性的必要性，但我们也回顾了一些更常用的BNP模型，从而有望在一定程度上回答两个问题：为什么以及如何使用BNP。
