Latent variable models and factors analysis
Latent variable models and factors analysis
Approximating posterior distributions in belief networks using mixtures
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to variational methods for graphical models
Learning in graphical models
Analysis of latent structure models with multidimensional latent variables
Statistics and neural networks
Bayesian learning in undirected graphical models: approximate MCMC algorithms
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Variational bayes estimation of mixing coefficients
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
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Latent structure models involve real, potentially observable variables and latent, unobservable variables. The framework includes various particular types of model, such as factor analysis, latent class analysis, latent trait analysis, latent profile models, mixtures of factor analysers, state-space models and others. The simplest scenario, of a single discrete latent variable, includes finite mixture models, hidden Markov chain models and hidden Markov random field models. The paper gives a brief tutorial of the application of maximum likelihood and Bayesian approaches to the estimation of parameters within these models, emphasising especially the fact that computational complexity varies greatly among the different scenarios. In the case of a single discrete latent variable, the issue of assessing its cardinality is discussed. Techniques such as the EM algorithm, Markov chain Monte Carlo methods and variational approximations are mentioned.