Global convergence of the EM algorithm for unconstrained latent variable models with categorical indicators

DOI10.1007/s11336-012-9295-zzbMath1284.62765OpenAlexW2083767805WikidataQ43853428 ScholiaQ43853428MaRDI QIDQ1940987

Alexander Weissman

Publication date: 11 March 2013

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-012-9295-z

zbMATH Keywords

convex optimizationvariational calculusrelative entropyEM algorithminformation theoryKullback-Leibler divergencelatent variable modelsoptimal boundslatent class models

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (2)

Optimizing information using the EM algorithm in item response theory ⋮ SCHMM-based modeling and prediction of random delays in networked control systems

Uses Software

• KNITRO
• AMPL

Cites Work

• A Modeling Language for Mathematical Programming
• On the convergence properties of the EM algorithm
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Statistical estimation from an optimization viewpoint
• A variational maximization-maximization algorithm for generalized linear mixed models with crossed random effects
• An introduction to variational methods for graphical models
• Variational approximations for categorical causal modeling with latent variables
• The EM Algorithm and Extensions, 2E
• Item Response Theory
• Elements of Information Theory
• Convex Analysis
• On Information and Sufficiency
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