Nonparametric empirical Bayes for the Dirichlet process mixture model
نویسندگان
چکیده
منابع مشابه
Nonparametric empirical Bayes for the Dirichlet process mixture model
The Dirichlet process prior allows flexible nonparametric mixture modeling. The number of mixture components is not specified in advance and can grow as new data come in. However, the behavior of the model is sensitive to the choice of the parameters, including an infinite-dimensional distributional parameter G0. Most previous applications have either fixed G0 as a member of a parametric family...
متن کاملBayesian Nonparametric Reliability Analysis Using Dirichlet Process Mixture Model
Cheng, Nan, M.S., August 2011, Industrial and Systems Engineering Bayesian Nonparametric Reliability Analysis Using Dirichlet Process Mixture Model Director of Thesis: Tao Yuan This thesis develops a Bayesian nonparametric method based on Dirichlet Process Mixture Model (DPMM) and Markov chain Monte Carlo (MCMC) simulation algorithms to analyze non-repairable reliability lifetime data. Kernel d...
متن کاملMarginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models
We present a method for comparing semiparametric Bayesian models, constructed under the Dirichlet process mixture (DPM) framework, with alternative semiparameteric or parameteric Bayesian models. A distinctive feature of the method is that it can be applied to semiparametric models containing covariates and hierarchical prior structures, and is apparently the rst method of its kind. Formally,...
متن کاملThe Dirichlet Process Mixture (DPM) Model
The Dirichlet distribution forms our first step toward understanding the DPM model. The Dirichlet distribution is a multi-parameter generalization of the Beta distribution and defines a distribution over distributions, i.e. the result of sampling a Dirichlet is a distribution on some discrete probability space. Let Θ = {θ1,θ2, . . . ,θn} be a probability distribution on the discrete space = { 1...
متن کاملDirichlet Process Mixture Model with Spatial Constraints
Dirichlet process (DP) provides a nonparametric prior for the mixture model that allows for the automatic detection of the number of hidden states. Recent introduction of variational Bayesian (VB) inference as a deterministic approach makes it practical to large-scale realworld problems. However, the models proposed so far have intrinsic limitations when used on noisy datasets and in situations...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics and Computing
سال: 2006
ISSN: 0960-3174,1573-1375
DOI: 10.1007/s11222-006-5196-2