Results 261 to 270 of about 23,712,988 (293)

Estimating Mixture of Dirichlet Process Models

Journal of Computational and Graphical Statistics, 1998
Abstract Current Gibbs sampling schemes in mixture of Dirichlet process (MDP) models are restricted to using “conjugate” base measures that allow analytic evaluation of the transition probabilities when resampling configurations, or alternatively need to rely on approximate numeric evaluations of some transition probabilities.
Steven N. Maceachern, Peter Müller
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Dirichlet Process Mixture Models for lexical category acquisition

2011 IEEE International Conference on Cloud Computing and Intelligence Systems, 2011
In this work, we apply Dirichlet Process Mixture Models (DPMMs) to a cognitive computational task in natural language processing (NLP): lexical category acquisition. The model takes a corpus of child-directed speech from CHILDES as input. We assess the performance using a new measure we proposed that meets three criteria: informativeness, diversity and
Bichuan Zhang, Xiaojie Wang 0006, Guannan Fang   +2 more
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Dirichlet process mixture models for insurance loss data

Scandinavian Actuarial Journal, 2017
In the recent insurance literature, a variety of finite-dimensional parametric models have been proposed for analyzing the hump-shaped, heavy-tailed, and highly skewed loss data often encountered in applications. These parametric models are relatively simple, but they lack flexibility in the sense that an actuary analyzing a new data set cannot be sure
Liang Hong, Ryan Martin
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Annealed SMC Samplers for Dirichlet Process Mixture Models

2010 20th International Conference on Pattern Recognition, 2010
In this work we propose a novel algorithm that approximates sequentially the Dirichlet Process Mixtures (DPM) model posterior. The proposed method takes advantage of the Sequential Monte Carlo (SMC) samplers framework to design an effective annealing procedure that prevents the algorithm to get trapped in a local mode.
Yener Ülker, Bilge Günsel, Ali Taylan Cemgil   +2 more
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Modeling of traffic data characteristics by Dirichlet Process Mixtures

2012 IEEE International Conference on Automation Science and Engineering (CASE), 2012
This paper presents a statistical method for modeling large volume of traffic data by Dirichlet Process Mixtures (DPM). Traffic signals are in general defined by their spatial-temporal characteristics, of which some can be common or similar across a set of signals, while a minority of these signals may have characteristics inconsistent with the ...
Ngan, YT, Yeh, AGO, Yung, NHC
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Dirichlet process mixture models for time-dependent clustering

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
In many problems of signal processing, an important task is the classification of data. A group of methods that has attracted much interest for this purpose are the nonparametric Bayesian methods, and in particular, those based on the Dirichlet process.
Kezi Yu, Petar M. Djuric
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Learning Label Distribution with Dirichlet Process Mixture Model

Proceedings of the AAAI Conference on Artificial Intelligence
Label Distribution Learning (LDL) is an effective machine learning paradigm for addressing label ambiguity, where each sample is annotated with a distribution that conveys rich semantic information. However, during the actual annotation process of label distributions, annotators often exhibit divergent labeling preferences for the same sample.
Minglong Wang, Weiwei Li 0001, Yunan Lu 0002, Xiuyi Jia   +3 more
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Dirichlet Process Mixture Model for Summarizing the Social Web

2015
Automatic summarizations have gained increasing attentions as they not only improve reading experiences but also facilitate management of collective knowledge on the social web. The social web is featured by social interactions. Ignoring this type of information limits the ability of traditional summarization techniques to generate more intelligent and
Xinjun Guan, Ying Yang, Xinru Yang, Chen Lin 0001   +3 more
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Dirichlet process mixture model, extensions and applications

Chinese Science Bulletin, 2012
Nonparametric Bayesian inference is a research topic in the fields of computational statistics and machine learning. The review conducts a comprehensive survey on one of the representative stochastic processes-Dirichlet process (DP), including theoretical fundamentals, representation & construction method, extensions, statistical inference and ...
Fei WANG, SuYu MEI, ShuiGeng ZHOU
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Classification of multivariate data using Dirichlet process mixture models

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012
We address the problem of multivariate data classification by the nonparametric Bayesian methodology, where the priors are modeled as Dirichlet processes. Sets of series of multivariate data are observed, where each vector in the series is modeled by a Gaussian linear regression.
Petar M. Djuric, André Ferrari
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