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Found 7 papers, 4 papers with code
Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count Data
A mixture of multivariate Poisson-log normal factor analyzers is introduced by imposing constraints on the covariance matrix, which resulted in flexible models for clustering purposes.
A parsimonious family of multivariate Poisson-lognormal distributions for clustering multivariate count data
Due to this hierarchical structure, the MPLN model can account for over-dispersion as opposed to the traditional Poisson distribution and allows for correlation between the variables.
Computation 62H30
Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data
Matrix variate distributions offer a natural way to model three-way data and mixtures of matrix variate distributions can be used to cluster three-way data.
Methodology
A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data
The aim of applying mixture model-based clustering in this context is to discover groups of co-expressed genes, which can shed light on biological functions and pathways of gene products.
Methodology Quantitative Methods Computation
Variational Bayes Approximations for Clustering via Mixtures of Normal Inverse Gaussian Distributions
Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations.
Constrained Optimization for a Subset of the Gaussian Parsimonious Clustering Models
Previous work has focused on circumventing this problem by constraining the smallest eigenvalue of the component covariance matrices.
A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting
Within the family setting, model selection involves choosing the member of the family, i. e., the appropriate covariance structure, in addition to the number of mixture components.
