Handbook of Mixture Analysis

Olga Moreira

• Publisher: Arcler Press
• Year: 2020
• eBook ISBN: 9781774079119
• Topic: Mathematics
• Subtopic: Mathematical Analysis
• Index: Mathematics

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).

Table of contents

• Cover
• Title Page
• Copyright
• DECLARATION
• ABOUT THE EDITOR
• TABLE OF CONTENTS
• List of Contributors
• List of Abbreviations
• Preface
• Chapter 1 Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation
• Chapter 2 A Fast Incremental Gaussian Mixture Model
• Chapter 3 Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models
• Chapter 4 Estimating Mixture Entropy with Pairwise Distances
• Chapter 5 Dependent Gaussian Mixture Models for Source Separation
• Chapter 6 A Two-Stage Approach Using Gaussian Mixture Models and Higher-Order Statistics for a Classification of Normal and Pathological Voices
• Chapter 7 PET Image Segmentation Using a Gaussian Mixture Model and Markov Random Fields
• Chapter 8 Kernel Analysis Based on Dirichlet Processes Mixture Models
• Chapter 9 A Mixture of Generalized Tukey’s
• Chapter 10 Poisson Mixture Regression Models for Heart Disease Prediction
• Chapter 11 A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data
• Chapter 12 Multi-Partitions Subspace Clustering
• Chapter 13 Shrinkage Simplex-Centroid Designs for a Quadratic Mixture Model
• Chapter 14 Determining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty
• Chapter 15 Mixture Models for Analyzing Product Reliability Data: A Case Study
• Chapter 16 Marginalized Mixture Models for Count Data from Multiple Source Populations
• Index
• Back Cover