Theory and Use of the EM Algorithm

DOI10.1561/2000000034zbMath1294.62137OpenAlexW4292080463WikidataQ56286735 ScholiaQ56286735MaRDI QIDQ3007983

Yi-Hua Chen, Maya R. Gupta

Publication date: 17 June 2011

Published in: Foundations and Trends® in Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1561/2000000034

zbMATH Keywords

hidden Markov modelsexpectation-maximization algorithmGaussian mixture modelscompound Dirichlet distributionsuperimposed signals

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Point estimation (62F10) Markov processes: estimation; hidden Markov models (62M05) Research exposition (monographs, survey articles) pertaining to statistics (62-02)

Related Items (10)

Likelihood-based quantile autoregressive distributed lag models and its applications ⋮ Practical Initialization of Recursive Mixture-Based Clustering for Non-negative Data ⋮ Gaussian processes for unconstraining demand ⋮ Unnamed Item ⋮ Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and expectation maximization ⋮ Growth mixture modeling with measurement selection ⋮ Learning the truth vector in high dimensions ⋮ Extreme clustering -- a clustering method via density extreme points ⋮ MMK for heterogeneous data: identifiability, estimation and application ⋮ Modeling the Stochastic Dynamics of Influenza Epidemics with Vaccination Control, and the Maximum Likelihood Estimation of Model Parameters

This page was built for publication: Theory and Use of the EM Algorithm