A tutorial on Dirichlet process mixture modeling
A tutorial on Dirichlet process mixture modeling
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DOI：
10.1016/j.jmp.2019.04.004
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发表时间：
2019-08-01
影响因子：
1.8
通讯作者：
Gonen, Mithat
中科院分区：
文献类型：
--
作者：
Li, Yuelin;Schofield, Elizabeth;Gonen, Mithat
贝叶斯非参数（BNP）模型在心理学中正变得越来越重要，无论是作为认知的理论模型还是作为分析工具。然而，现有的教程往往处于非技术人员无法理解的抽象层次。本教程旨在帮助初学者通过仔细和明确地处理重要但经常忽略的推导来理解关键概念，重点是将数学与Dirichlet过程混合模型（DPMM）的实际计算解决方案联系起来-这是最广泛使用的BNP方法之一。抽象概念通过研究产生它们的理论而变得明确和具体。一个用统计语言R编写的可公开访问的计算机程序被逐行解释，以帮助读者理解计算算法。该算法还与本杂志中的一个可访问教程中的一种称为中国餐馆过程的构造方法相关联（Gershman和Blei，2012）。总体目标是帮助读者更全面地了解理论和应用，以便他们可以在自己的工作中应用BNP方法，并利用本教程中的技术细节来开发新的方法。(C)2019爱思唯尔公司All rights reserved.
Bayesian nonparametric (BNP) models are becoming increasingly important in psychology, both as theoretical models of cognition and as analytic tools. However, existing tutorials tend to be at a level of abstraction largely impenetrable by non-technicians. This tutorial aims to help beginners understand key concepts by working through important but often omitted derivations carefully and explicitly, with a focus on linking the mathematics with a practical computation solution for a Dirichlet Process Mixture Model (DPMM)-one of the most widely used BNP methods. Abstract concepts are made explicit and concrete to non-technical readers by working through the theory that gives rise to them. A publicly accessible computer program written in the statistical language R is explained line-by-line to help readers understand the computation algorithm. The algorithm is also linked to a construction method called the Chinese Restaurant Process in an accessible tutorial in this journal (Gershman and Blei, 2012). The overall goals are to help readers understand more fully the theory and application so that they may apply BNP methods in their own work and leverage the technical details in this tutorial to develop novel methods. (C) 2019 Elsevier Inc. All rights reserved.