Everything about Mixture model
Statistical concept
In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with "mixture distributions" relate to deriving the properties of the overall population from those of the sub-populations, "mixture models" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information. Mixture models are used for clustering, under the name model-based clustering, and also for density estimation. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Mixture modelA 5-minute orientation
- Skim a recent paper: The Bibliometric Analysis on Finite Mixture ModelInfluential work since 2022
- Pick up a classic book: Vaccines for the 21st centuryThe most-reprinted book on the subject
- Read the landmark paper: Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation StudyCited 10,168 times
- Go sideways: Dirichlet processA closely related topic
- Go sideways: Dirichlet-multinomial distributionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study
On a Mixture Autoregressive Model
Superpixel Segmentation Using Gaussian Mixture Model
A flexible extreme value mixture model
Bounded generalized Gaussian mixture model
Robust parameter estimation for mixture model
A self-adaptive Gaussian mixture model
Gaussian mixture 3D morphable face model
🆕 Influential since 2022
📚 Books worth reading
Vaccines for the 21st centuryInstitute of Medicine (U.S.). Committee to Study Priorities for Vaccine Development, Committee to Study Priorities for Vaccine Development · 2000
Finite Mixture and Markov Switching ModelsSylvia Frühwirth-Schnatter · 2006
Theory of structured multiphase mixturesFlavio Dobran · 1991
Medical Applications of Finite Mixture ModelsPeter Schlattmann · 2009
Testing lack of fit in a mixture modelJohn Thomas Shelton · 1982- 📕Misaligned (shuffled) data analysis with application to gene regulationCharles Lawrence Gellert · 1990
A primer on experiments with mixturesCornell, John A. · 2011- 📕Lead in petrolBrian Price · 1982
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Dirichlet process In probability theory, Dirichlet processes (after the distribution associated with Peter Gustav Lejeune Dirichlet) are a family of stochastic processes…
- Dirichlet-multinomial distribution In probability theory and statistics, the Dirichlet-multinomial distribution is a family of discrete multivariate probability distributions on a finite…
- Variational Bayesian methods Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They…
- Gibbs sampling In statistics, Gibbs sampling or a Gibbs sampler, also known in statistical mechanics as the heat bath algorithm, is a Markov chain Monte Carlo (MCMC)…
- Latent Dirichlet allocation In natural language processing, latent Dirichlet allocation (LDA) is a generative statistical model that explains how a collection of text documents can…
- Hierarchical Dirichlet process In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. It uses a…
- Expectation–maximization algorithm In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates…
- Dirichlet distribution In probability and statistics, the Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted Dir(𝜶), is a family of continuous multivariate…