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.
Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies.
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
Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations.
Previous work has focused on circumventing this problem by constraining the smallest eigenvalue of the component covariance matrices.
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.