Variational approximations for categorical causal modeling with latent variables

DOI10.1007/BF02294734zbMath1306.62434MaRDI QIDQ2259879

Yanyan Li

Publication date: 5 March 2015

Published in: Psychometrika (Search for Journal in Brave)

zbMATH Keywords

EM algorithmvariational approximationlatent classcausal model

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (9)

Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes ⋮ Variational Bayes inference for hidden Markov diagnostic classification models ⋮ Fitting an item response theory model with random item effects across groups by a variational approximation method ⋮ A variational maximization-maximization algorithm for generalized linear mixed models with crossed random effects ⋮ Global convergence of the EM algorithm for unconstrained latent variable models with categorical indicators ⋮ Scalable Bayesian approach for the DINA Q-matrix estimation combining stochastic optimization and variational inference ⋮ Item screening in graphical loglinear Rasch models ⋮ Latent class models for diary method data: Parameter estimation by local computations ⋮ Mean-field variational approximate Bayesian inference for latent variable models

Uses Software

• LISREL
• EQS
• BMDP

Cites Work

• Probability functions on complex pedigrees
• The EM algorithm for graphical association models with missing data
• Efficient estimation and local identification in latent class analysis
• Factorial hidden Markov models
• Improving the mean-field approximation in belief networks using Bahadur's reparameterisation of the multivariate binary distribution
• The computational complexity of probabilistic inference using Bayesian belief networks
• Probabilistic Independence Networks for Hidden Markov Probability Models
• On the Adequacy of Variational Lower Bound Functions for Likelihood-based Inference in Markovian Models with Missing Values
• Exploratory latent structure analysis using both identifiable and unidentifiable models
• Asymptotically distribution‐free methods for the analysis of covariance structures
• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
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