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Correction to: For antibody sequence generative modeling, mixture models may be all you need. [PDF]
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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 Leemaqz
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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 Leemaqz
exaly +5 more sources
Computational Statistics & Data Analysis, 2007
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Dankmar Böhning +5 more
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Dankmar Böhning +5 more
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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 ...
BISHOP CHRISTOPHER M +1 more
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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.
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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.
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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 +2 more
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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 +2 more
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On Clustering by Mixture Models
2003Finite 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.
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Mixture Models of Categorization
Journal of Mathematical Psychology, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Dirichlet Process Mixture of Mixtures Model for Unsupervised Subword Modeling
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018We 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 +2 more
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