Splitting and merging components of a nonconjugate Dirichlet process mixture model
نویسندگان
چکیده
منابع مشابه
Splitting and Merging Components of a Nonconjugate Dirichlet Process Mixture Model
Abstract. The inferential problem of associating data to mixture components is difficult when components are nearby or overlapping. We introduce a new split-merge Markov chain Monte Carlo technique that efficiently classifies observations by splitting and merging mixture components of a nonconjugate Dirichlet process mixture model. Our method, which is a Metropolis-Hastings procedure with split...
متن کاملSequentially-Allocated Merge-Split Sampler for Conjugate and Nonconjugate Dirichlet Process Mixture Models
This paper proposes a new efficient merge-split sampler for both conjugate and nonconjugate Dirichlet process mixture (DPM) models. These Bayesian nonparametric models are usually fit usingMarkov chain Monte Carlo (MCMC) or sequential importance sampling (SIS). The latest generation of Gibbs and Gibbs-like samplers for both conjugate and nonconjugate DPM models effectively update the model para...
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 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 کاملA Dirichlet Process Mixture Model for Spherical Data
Directional data, naturally represented as points on the unit sphere, appear in many applications. However, unlike the case of Euclidean data, flexible mixture models on the sphere that can capture correlations, handle an unknown number of components and extend readily to high-dimensional data have yet to be suggested. For this purpose we propose a Dirichlet process mixture model of Gaussian di...
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: 2007
ISSN: 1936-0975
DOI: 10.1214/07-ba219