Moreira Handbook of Mixture Analysis

Erscheinungsjahr 2020
ISBN: 978-1-77407-911-9
Verlag: Arcler Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 398 Seiten, Electronic book text, Format (B × H): 152 mm x 229 mm

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).

Autoren/Hrsg.

Moreira, Olga

Fachgebiete

• Mathematik | Informatik Mathematik Mathematische Analysis

Inhaltsverzeichnis

- 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 Distributions
- 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

Über Autor(innen)

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.