Search Results for author: Sanjeena Subedi

Found 6 papers, 3 papers with code

A parsimonious family of multivariate Poisson-lognormal distributions for clustering multivariate count data

1 code implementation15 Apr 2020 Sanjeena Subedi, Ryan Browne

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

1 code implementation22 Jul 2018 Anjali Silva, Steven J. Rothstein, Paul D. McNicholas, Sanjeena Subedi

Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies.


A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data

2 code implementations30 Nov 2017 Anjali Silva, Steven J. Rothstein, Paul D. McNicholas, Sanjeena Subedi

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

no code implementations7 Sep 2013 Sanjeena Subedi, Paul D. McNicholas

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

no code implementations25 Jun 2013 Ryan P. Browne, Sanjeena Subedi, Paul McNicholas

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

no code implementations23 Jun 2013 Sanjeena Subedi, Paul D. McNicholas

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.

Model Selection

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