Results 211 to 220 of about 16,235,673 (260)

PI gain tuning for pressure-based MFCs with Gaussian mixture model. [PDF]

Sci Rep
Higuchi S, Ueda T, Takijiri K, Hayashi D.   +3 more
europepmc   +1 more source

A novel trajectory learning method for robotic arms based on Gaussian Mixture Model and k-value selection algorithm. [PDF]

PLoS One
Yan J, Wu Y, Ji K, Cheng C, Zheng Y.
europepmc   +1 more source

Multiscale Sample Entropy-Based Feature Extraction with Gaussian Mixture Model for Detection and Classification of Blue Whale Vocalization. [PDF]

Entropy (Basel)
Babalola OP, Ogundile OO, Balyan V.
europepmc   +1 more source

A Bayesian beta-binomial piecewise growth mixture model for longitudinal overdispersed binomial data. [PDF]

Stat Methods Med Res
Wen CC, Baker N, Paul R, Hill E, Hunt K, Li H, Gray K, Neelon B.   +7 more
europepmc   +1 more source

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Finite Mixture Models

Annual Review of Statistics and Its Application, 2019
The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and general scientific literature. The aim of this article is to provide an up-to-date account of the theory and methodological developments underlying the ...
Geoffrey McLachlan, Sharon Lee
exaly   +5 more sources

Advances in Mixture Models

Computational Statistics & Data Analysis, 2007
International ...
Dankmar Böhning, Wilfried Seidel, Marco Alfò, Bernard Garel, Valentin Patilea, Günther Walther   +5 more
openaire   +3 more sources

Mixture Modeling

2019
This chapter is concerned with methods for the analysis of data on outbreaks of infectious disease in which additional genomic information is available on the pathogen. Molecular typing data from viruses or bacteria isolated from individual patients can contain additional information on possible links in the contact network that have led to ...
Del Fava, E., Shkedy, Z.
openaire   +2 more sources

On Clustering by Mixture Models

2003
Finite mixture models are being increasingly used to model the distributions of a wide variety of random phenomena and to cluster data sets; see, for example, McLachlan and Peel (2000a). We consider the use of normal mixture models to cluster data sets of continuous multivariate data, concentrating on some of the associated computational issues.
McLachlan, G. J., Ng, A.S. K., Peel, D.
openaire   +4 more sources

Robust Bayesian mixture modelling

Neurocomputing, 2005
Bayesian approaches to density estimation and clustering using mixture distributions allow the automatic determination of the number of components in the mixture. Previous treatments have focussed on mixtures having Gaussian components, but these are well known to be sensitive to outliers, which can lead to excessive sensitivity to small numbers of ...
BISHOP CHRISTOPHER M, SVENSEN JOHAN FREDRIK MARKUS   +1 more
openaire   +4 more sources