Results 181 to 190 of about 31,307,485 (221)

Models with products of Dirichlet processes

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
Nonparametric Bayesian models are often preferred over parametric models due to their superior flexibility in interpreting data. A strong motivation for the use of these models is the desire of avoiding the assumptions that are necessary for parametric models.
Petar M. Djuric, André Ferrari
openaire   +2 more sources

Intelligent Cooperative Spectrum Sensing via Hierarchical Dirichlet Process in Cognitive Radio Networks

IEEE Journal on Selected Areas in Communications, 2015
Cognitive radio (CR) is a critical technology for improving spectrum utilization and solving the radio spectrum scarcity problem. In CR devices, spectrum sensing is important to implement opportunistic spectrum access.
Xin-Lin Huang, Fei Hu, Jun Wu, Hsiao-Hwa Chen, G. Wang, Tao Jiang   +5 more
semanticscholar   +1 more source

Weak Convergence of a Dirichlet-Multinomial Process

gmj, 2003
Abstract We present a random probability distribution which approximates, in the sense of weak convergence, the Dirichlet process and supports a Bayesian resampling plan called a proper Bayesian bootstrap.
MULIERE, PIETRO, SECCHI P.
openaire   +4 more sources

Computations of Mixtures of Dirichlet Processes

SIAM Journal on Scientific and Statistical Computing, 1986
The computation of Bayes estimators based on mixtures of Dirichlet processes is treated. These estimators may be written as ratios of two multidimensional integrals, each of which may be decomposed into a weighted average of products of one-dimensional integrals.
openaire   +2 more sources

Variational learning for Dirichlet process mixtures of Dirichlet distributions and applications

Multimedia Tools and Applications, 2012
In this paper, we propose a Bayesian nonparametric approach for modeling and selection based on a mixture of Dirichlet processes with Dirichlet distributions, which can also be seen as an infinite Dirichlet mixture model. The proposed model uses a stick-breaking representation and is learned by a variational inference method.
Wentao Fan 0001, Nizar Bouguila
openaire   +2 more sources

A Dirichlet process biterm-based mixture model for short text stream clustering

Applied intelligence (Boston), 2020
Junyang Chen, Zhi-Guo Gong, Weiwen Liu
semanticscholar   +1 more source

Tree-Structured Hierarchical Dirichlet Process

2019
In many domains, document sets are hierarchically organized such as message forums having multiple levels of sections. Analysis of latent topics within such content is crucial for tasks like trend and user interest analysis. Nonparametric topic models are a powerful approach, but traditional Hierarchical Dirichlet Processes (HDPs) are unable to fully ...
Alam, Md Hijbul, Peltonen, Jaakko, Nummenmaa, Jyrki, Järvelin, Kalervo   +4 more
openaire   +3 more sources

A characterization of the Dirichlet process

Statistics & Probability Letters, 1991
The purpose of this note is to point out that if the posterior mean of a random probability \(P\) given a sample \(X_ 1,\ldots,X_ n\) from \(P\) is linear in the sample empirical distribution function, then \(P\) is a Dirichlet random probability.
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Markov Chain Sampling Methods for Dirichlet Process Mixture Models

, 2000
Radford M. Neal
semanticscholar   +1 more source

Dirichlet Process

2021
Nezameddin Faghih, Ebrahim Bonyadi, Lida Sarreshtehdari   +2 more
openaire   +1 more source