· 2019
Handbook of Mixture Analysis
- 222citations
- 2019year
Short summary
This handbook provides a broad overview of the methods and applications of mixture models, a versatile statistical tool used across many disciplines for over 150 years.
AI-generated from the title and abstract; the full text is not read.
Key points
- Mixture models are a versatile statistical tool used for over 150 years.
- They are applicable to a wide range of data types including univariate, multivariate, continuous, and categorical.
- The handbook covers key methods like the EM algorithm and Bayesian mixture models.
- Applications span finance, genomics, and astronomy.
AI-generated from the title and abstract; the full text is not read.
Abstract
Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time.The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy.
The authors' abstract, as published at the source., 2019 · DOI ↗
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