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Mathematics and Statistics

Handbook Of Mixture Analysis

Handbook Of Mixture Analysis

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The book "Handbook of Mixture Analysis" is a collection of peer-reviewed articles featuring several statistical data analysis methods based on mixture models and their applications in scientific domains such as data mining, machine learning, physics, mechanical engineering, signal processing, economics, cosmology, computational medicine, and more. This book covers several aspects of mixture analysis and variety of models such as the Gaussian Mixture Model (GMM), Dirichlet processes Mixture Model (DMM), Poisson Mixture Regression Model (PMRM), Hierarchical Gamma Mixture Model (HGMM), Quadratic Mixture Model (QMM), K-fold Mixture Model (KMM), Finite Mixture Model (FMM), and Multi-partitions Subspace Mixture Model (M-SMM).

Olga Moreira is a Ph.D. in Astrophysics and B.Sc. in Physics and Applied Mathematics. She is an experienced technical writer and researcher which former fellowships include postgraduate positions at two of the most renown European institutions in the fields of Astrophysics and Space Science (the European Southern Observatory, and the European Space Agency). Presently, she is an independent scientist working on projects involving machine learning and neural networks research as well as peer-reviewing and edition of academic books.