Parameter estimation in latent profile models
β Scribed by A.P. Dunmur; D.M. Titterington
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 911 KB
- Volume
- 27
- Category
- Article
- ISSN
- 0167-9473
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β¦ Synopsis
Latent profile analysis is a version of latent structure analysis in which the observed variables are continuous and the latent variables are discrete. The latent structure can be enriched if the latent variables are multivariate, but computational difficulties can arise in the implementation of the appropriate version of the EM algorithm. These difficulties can be eased by incorporating mean-field approximations in the E-step of the EM algorithm. Simple examples, treated in detail, show the effectiveness of these methods, which were first proposed in the engineering and neural-computing literatures.
π SIMILAR VOLUMES
An adaptive algorithm is developed for estimating the parameters of a linear multivariable error model with coupled dynamics, using estimation errors for coupling inputs. Coupled dynamics in an error equation lead to a new type of error models which have different regressors but the same parameter e