Results 171 to 180 of about 31,307,485 (221)
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IEEE transactions on multimedia, 2019
Depression causes mood disorders with noticeable problems in day-to-day activities. Current methods of assessing depression depend almost entirely on clinical interviews or questionnaires.
Lang He, Dong-Mei Jiang, H. Sahli
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Depression causes mood disorders with noticeable problems in day-to-day activities. Current methods of assessing depression depend almost entirely on clinical interviews or questionnaires.
Lang He, Dong-Mei Jiang, H. Sahli
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On Non-Continuous Dirichlet Processes
Journal of Theoretical Probability, 2003A Dirichlet process is an adapted càdlàg process that can be represented as the sum of a semimartingale and an adapted continuous process with zero quadratic variation. For continuous Dirichlet processes, a pathwise Itô calculus was introduced by \textit{H. Föllmer} [in: Séminaire de probabilités XV. Lect. Notes Math. 850, 143-150 (1981; Zbl 0461.60074)
Leszek Słomiński
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Chemical Engineering and Science, 2019
Data driven soft sensors have found widespread applications in chemical processes for predicting those important yet difficult-to-measure quality variables.
Wei-Ming Shao, Zhiqiang Ge, Zhihuan Song
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Data driven soft sensors have found widespread applications in chemical processes for predicting those important yet difficult-to-measure quality variables.
Wei-Ming Shao, Zhiqiang Ge, Zhihuan Song
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Axially Symmetric Data Clustering Through Dirichlet Process Mixture Models of Watson Distributions
IEEE Transactions on Neural Networks and Learning Systems, 2019This paper proposes a Bayesian nonparametric framework for clustering axially symmetric data. Our approach is based on a Dirichlet processes mixture model with Watson distributions, which can also be considered as the infinite Watson mixture model.
Wentao Fan +3 more
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Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia
IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, 2019Due to the increasing availability of large data sets, the need for general-purpose massively-parallel analysis tools become ever greater. In unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet-Process Mixture Model
Or Dinari +3 more
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Dependent Dirichlet Process Modeling and Identity Learning for Multiple Object Tracking
Asilomar Conference on Signals, Systems and Computers, 2018We consider the problem of tracking multiple objects with unknown state parameter information, time-dependent cardinality, and object identity. The problem becomes even more challenging when the unordered measurements have a large number of false alarms ...
Bahman Moraffah +1 more
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Dependent mixtures of Dirichlet processes
Computational Statistics & Data Analysis, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Spyridon J. Hatjispyros +2 more
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Journal of the American Statistical Association, 2008
In multicenter studies, subjects in different centers may have different outcome distributions. This article is motivated by the problem of nonparametric modeling of these distributions, borrowing information across centers while also allowing centers to be clustered.
Abel Rodriguez +2 more
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In multicenter studies, subjects in different centers may have different outcome distributions. This article is motivated by the problem of nonparametric modeling of these distributions, borrowing information across centers while also allowing centers to be clustered.
Abel Rodriguez +2 more
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Conjugacy as a Distinctive Feature of the Dirichlet Process
Scandinavian Journal of Statistics, 2006The authors introduce a class of normalized homogeneous random measures with independent increments (normal HRMI). These measures are obtained by normalization of time-dependent subordinators. Formulas for the variance-covariance structure and the skewness of such measures are derived.
Antonio Lijoi, Igor Prünster
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Robust Dirichlet Process mixtures
2011 Seventh International Conference on Natural Computation, 2011Non-parametric Dirichlet Process mixture (DPM) approaches for density estimation and clustering allow for automatic model selection. In this paper, we aim to develop robust DPM algorithm for clustering datasets with scatter objects, or outliers. In the developed mean-field variational inference algorithms, the auxiliary posterior distributions are ...
Jianyong Sun, Jonathan M. Garibaldi
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