Results 231 to 240 of about 1,959,808 (288)

Improvement of the Gaussian mixture models' unsupervised learning method through the inclusion of dynamical systems for various types of nonlinear data. [PDF]

Heliyon
Mahjoub R.
europepmc   +1 more source

Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in onco-hematology. [PDF]

PLoS Comput Biol
Dall'Olio D, Sträng E, Turki AT, Tettero JM, Barbus M, Schulze-Rath R, Elicegui JM, Matteuzzi T, Merlotti A, Carota L, Sala C, Della Porta MG, Giampieri E, Hernández-Rivas JM, Bullinger L, Castellani G, with the HARMONY Healthcare Alliance Consortium.   +16 more
europepmc   +1 more source

Role of Pain Catastrophizing in the Effects of Cognitive Behavioral Therapy for Chronic Pain in Different Subgroups: An Exploratory Secondary Data Analysis Using Finite Mixture Models. [PDF]

Mil Med
Wi D, Ransom JC, Flynn DM, Steffen AD, Park C, Burke LA, Doorenbos AZ.   +6 more
europepmc   +1 more source

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models. [PDF]

J Am Stat Assoc
Wang Y, Degleris A, Williams A, Linderman SW.   +3 more
europepmc   +1 more source

Correction to: For antibody sequence generative modeling, mixture models may be all you need. [PDF]

Bioinformatics
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 J McLachlan
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   +2 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

Robust Bayesian mixture modelling [PDF]

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 ...
Markus Svensén, Christopher M. Bishop
openaire   +1 more source