Results 261 to 270 of about 15,569,368 (309)

Predicting brain health in community-dwelling elderly populations by integrating Gaussian mixture model and plasma biomarkers. [PDF]

J Alzheimers Dis Rep
Wang Y, Zhu T, Cheng Q, Cui X, Zhang P, Lu Z, Alzheimer's Disease Neuroimaging Initiative (ADNI) *.   +6 more
europepmc   +1 more source

Multiscale homogenized constrained mixture model of the bio-chemo-mechanics of soft tissue growth and remodeling. [PDF]

Biomech Model Mechanobiol
Paukner D, Humphrey JD, Cyron CJ.
europepmc   +1 more source

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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

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 ...
McLachlan, Geoffrey J., Lee, Sharon X., Rathnayake, Suren I.   +2 more
openaire   +4 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
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Dirichlet Process Mixture of Mixtures Model for Unsupervised Subword Modeling

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018
We develop a parallelizable Markov chain Monte Carlo sampler for a Dirichlet process mixture of mixtures model. Our sampler jointly infers a codebook and clusters. The codebook is a global collection of components. Clusters are mixtures, defined over the codebook.
Michael Heck, Sakriani Sakti, Satoshi Nakamura 0001   +2 more
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On Fitting Mixture Models

1999
Consider the problem of fitting a finite Gaussian mixture, with an unknown number of components, to observed data. This paper proposes a new minimum description length (MDL) type criterion, termed MMDL(f or mixture MDL), to select the number of components of the model.
Mário A. T. Figueiredo, José M. N. Leitão, Anil K. Jain 0001   +2 more
openaire   +1 more source