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Parameterization of a Two-Component Gaussian Mixture for Description of the Sea Surface
A new approach is proposed for calculating the parameters of a two-component Gaussian mixture for modeling the distribution of sea surface...
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Introduction to Mixture Models
In Chap. 1, we introduce the fundamental concept of a statistical model and provide a detailed definition of both mixture models and finite mixture...
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Parsimonious ultrametric Gaussian mixture models
Gaussian mixture models represent a conceptually and mathematically elegant class of models for casting the density of a heterogeneous population...
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Maximum Likelihood Estimation Under Finite Mixture Models
Chapter 3 centers our attention on finite mixture models. Within this framework, we establish that the consistent results achieved with the maximum...
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Improving the study of plant evolution with multi-matrix mixture models
Amino acid substitution model is a key component to study the plant evolution from protein sequences. Although single-matrix amino acid substitution...
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Semiparametric finite mixture of regression models with Bayesian P-splines
Mixture models provide a useful tool to account for unobserved heterogeneity and are at the basis of many model-based clustering methods. To gain...
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Machine learning embedded EM algorithms for semiparametric mixture regression models
In this article, we propose two machine learning embedded algorithms for a class of semiparametric mixture models, where the mixing proportions and...
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Excitation of a two-component mixture of a Peregrine bump and a dark or bright pulse for the partially nonlocal coupled NLSM in BEC and optics
We put our center of attention into a (2+1)-dimensional nonautonomous partially nonlocal coupled nonlinear Schrödinger model (NLSM) under a linear...
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The parsimonious Gaussian mixture models with partitioned parameters and their application in clustering
Cluster analysis is a method that identifies similar groups of data without any prior knowledge of the relevant groups. One of the most widely used...
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Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation
Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), involves different methods aiming to distinguish the individual contribution of...
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em-Test for Univariate Finite Gaussian Mixture Models
While the success of the EM-test in the previous two chapters was confined to finite mixture models with subpopulation distributions belonging to a...
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Dirichlet compound negative multinomial mixture models and applications
In this paper, we consider an alternative parametrization of Dirichlet Compound Negative Multinomial (DCNM) using rising polynomials. The new...
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Mixture Model
A mixture model is a probability model for representing subpopulations within a data set. The mixture model is built up from a weighted combination...
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Analysis of estimating the Bayes rule for Gaussian mixture models with a specified missing-data mechanism
Semi-supervised learning approaches have been successfully applied in a wide range of engineering and scientific fields. This paper investigates the...
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Estimation Under Finite Normal Mixture Models
The finite normal mixture model stands out as the most frequently employed model in statistical applications. Nevertheless, it exhibits some peculiar...
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Multivariate Density Estimation with Deep Neural Mixture Models
Albeit worryingly underrated in the recent literature on machine learning in general (and, on deep learning in particular), multivariate density...
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Mixture and Latent Class Models
This chapter introduces mixture models and latent class models. After a motivating example, formal definitions of these models are presented in Sect....
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Voellmy-type mixture rheologies for dilatant, two-layer debris flow models
We formulate and test different Voellmy-type mixture rheologies that can be introduced into two-layer debris flow models. The formulations are based...
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Heterogeneous analysis for clustered data using grouped finite mixture models
It is common to observe significant heterogeneity in clustered data across scientific fields. Cluster-wise conditional distributions are widely used...
