Mixture Model

This page contains resources about Mixture Models and the Expectation-Maximization Algorithm.

Subfields and Concepts

 * Bayesian Models
 * Non-Bayesian Models
 * Mixture of Experts
 * k-Nearest Neighbour (k-NN)
 * Gaussian Mixture Models
 * Mixture of Factor Analyzers
 * Mixture of Dimensionality Reducers
 * Latent Variable Models
 * Sparse EM Algorithm
 * Baum-Welch Algorithm (i.e. EM when applied to HMMs)
 * Generalized (Incomplete) EM Algorithm
 * Monte Carlo EM
 * Variational EM
 * Variational Bayesian EM (VBEM)

Books and Book Chapters

 * Theodoridis, S. (2015). "Chapter 12.5: Latent Variables and the EM Algorithm" Machine Learning: A Bayesian and Optimization Perspective. Academic Press.


 * Murphy, K. P. (2012). "Chapter 11: Mixture models and the EM algorithm ". Machine Learning: A Probabilistic Perspective. MIT Press.


 * Barber, D. (2012). "Chapter 11: Learning with Hidden Variables". Bayesian Reasoning and Machine Learning. Cambridge University Press.


 * Barber, D. (2012). "Chapter 20: Mixture Models". Bayesian Reasoning and Machine Learning. Cambridge University Press.
 * McLachlan, G., & Krishnan, T. (2007). The EM algorithm and extensions (Vol. 382). John Wiley & Sons.
 * Bishop, C. M. (2006). "Chapter 9: Mixture Models and EM". Pattern Recognition and Machine Learning. Springer.
 * McLachlan, G., & Peel, D. (2004). Finite mixture models. John Wiley & Sons.

Software

 * Nearest Neighbors (Statistics and Machine Learning Toolbox) - MATLAB
 * Gaussian Mixture Models (Statistics and Machine Learning Toolbox) - MATLAB

Other Resources

 * Mixture Models - Notebook