A tutorial on Bayesian nonparametric models

被引：382
作者：
Gershman, Samuel J. [1 ,2 ]
Blei, David M. [3 ]
机构：
[1] Princeton Univ, Dept Psychol, Princeton, NJ 08540 USA
[2] Princeton Univ, Princeton Neurosci Inst, Princeton, NJ 08540 USA
[3] Princeton Univ, Dept Comp Sci, Princeton, NJ 08540 USA
来源：
JOURNAL OF MATHEMATICAL PSYCHOLOGY | 2012年 / 56卷 / 01期
基金：
美国国家科学基金会;
关键词：
Bayesian methods; Chinese restaurant process; Indian buffet process; DIRICHLET; INFERENCE; SELECTION; MIXTURES; TIMES;
D O I：
10.1016/j.jmp.2011.08.004
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
A key problem in statistical modeling is model selection, that is, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number of clusters in mixture models or the number of factors in factor analysis. In this tutorial, we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application. (C) 2011 Elsevier Inc. All rights reserved.
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收藏
页码：1 / 12
页数：12
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