Markov switching Dirichlet process mixture regression
نویسندگان
چکیده
منابع مشابه
Markov Switching Dirichlet Process Mixture Regression
Markov switching models can be used to study heterogeneous populations that are observed over time. This paper explores modeling the group characteristics nonparametrically, under both homogeneous and nonhomogeneous Markov switching for group probabilities. The model formulation involves a finite mixture of conditionally independent Dirichlet process mixtures, with a Markov chain defining the m...
متن کاملMarkov Chain Sampling Methods for Dirichlet Process Mixture Models
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your perso...
متن کاملAdditive Cubic Spline Regression with Dirichlet Process Mixture Errors
The goal of this paper is to develop a flexible Bayesian analysis of regression models for continuous and categorical outcomes. In the models we study, covariate (or regression) effects are modeled additively by cubic splines, and the error distribution (that of the latent outcomes in the case of categorical data) is modeled as a Dirichlet process mixture. We employ a relatively unexplored but ...
متن کاملSpike-and-Slab Dirichlet Process Mixture Models
In this paper, Spike-and-Slab Dirichlet Process (SS-DP) priors are introduced and discussed for non-parametric Bayesian modeling and inference, especially in the mixture models context. Specifying a spike-and-slab base measure for DP priors combines the merits of Dirichlet process and spike-and-slab priors and serves as a flexible approach in Bayesian model selection and averaging. Computationa...
متن کاملEstimating Mixture of Dirichlet Process Models
Current Gibbs sampling schemes in mixture of Dirichlet process (MDP) models are restricted to using \conjugate" base measures which allow analytic evaluation of the transition probabilities when resampling con gurations, or alternatively need to rely on approximate numeric evaluations of some transition probabilities. Implementation of Gibbs sampling in more general MDP models is an open and im...
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: Bayesian Analysis
سال: 2009
ISSN: 1936-0975
DOI: 10.1214/09-ba430