Results 41 to 50 of about 11,564,802 (285)

Normalized random measures driven by increasing additive processes [PDF]

, 2004
This paper introduces and studies a new class of nonparametric prior distributions. Random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes.
PRUENSTER, IGOR, Walker, Stephen G., Nieto Barajas, Luis E., Nieto-Barajas, Luis E., Prunster, Igor   +4 more
core   +1 more source

Dirichlet Process Mixtures of Generalized Linear Models

J. Mach. Learn. Res., 2009
We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regression mean function estimate.
Lauren Hannah, David M. Blei, Warren B. Powell   +2 more
openaire   +4 more sources

Posterior asymptotics for boosted hierarchical Dirichlet process mixtures [PDF]

J. Mach. Learn. Res., 2022
Summary: Bayesian hierarchical models are powerful tools for learning common latent features across multiple data sources. The Hierarchical Dirichlet Process (HDP) is invoked when the number of latent components is a priori unknown. While there is a rich literature on finite sample properties and performance of hierarchical processes, the analysis of ...
Catalano, Marta, De Blasi, Pierpaolo, Lijoi, Antonio, Pruenster, Igor   +3 more
openaire   +3 more sources

Discovering transcriptional modules by Bayesian data integration [PDF]

, 2010
Motivation: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data.
Richard S. Savage, Wild, David L., Griffin, JE, Savage, RS, Bernard J. de la Cruz, Savage, Richard S., ,, De la Cruz, Bernard J., David L. Wild, de la Cruz, BJ, Wild, DL, Jim E. Griffin, Ghahramani, Z, Ghahramani, Zoubin, Griffin, Jim E., Zoubin Ghahramani   +15 more
core   +1 more source

Small Area Estimation under Poisson–Dirichlet Process Mixture Models

Axioms
In this paper, we propose an improved Nested Error Regression model in which the random effects for each area are given a prior distribution using the Poisson–Dirichlet Process. Based on this model, we mainly investigate the construction of the parameter
Xiang Qiu, Qinchun Ke, Xueqin Zhou, Yulu Liu   +3 more
doaj   +1 more source

Dirichlet Process Mixtures of Generalized Mallows Models

CoRR, 2012
We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters.
Marina Meila, Harr Chen
openaire   +3 more sources

A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency

Entropy, 2018
Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge.
Xulun Ye, Jieyu Zhao, Yu Chen
doaj   +1 more source

Background subtraction using infinite asymmetric Gaussian mixture models with simultaneous feature selection

IET Image Processing, 2020
Mixture models are broadly applied in image processing domains. Related existing challenges include failure to approximate exact data shapes, estimate correct number of components, and ignore irrelevant features.
Ziyang Song, Samr Ali, Nizar Bouguila
doaj   +1 more source

A Dirichlet Process Mixture Model for Non-Ignorable Dropout [PDF]

Bayesian Analysis, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Moore, Camille M., Carlson, Nichole E., MaWhinney, Samantha, Kreidler, Sarah   +3 more
openaire   +2 more sources

Models beyond the Dirichlet process [PDF]

Bayesian nonparametric inference is a relatively young area of research and it has recently undergone a strong development. Most of its success can be explained by the considerable degree of exibility it ensures in statistical modelling, if compared to ...
Antonio Lijoi, Igor Pruenster
core   +4 more sources