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Latent class analysis identification of syndromes in Alzheimer's disease: A Bayesian approach

Date
2006
Abstract
Latent variable models have been used extensively in the social sciences. In this work a latent class analysis is used to identify syndromes within Alzheimer's disease. The fitting of the model is done in a Bayesian framework, and this is examined in detail here. In particular, the label switching problem is identified, and solutions presented. Graphical summaries of the posterior distribution are included.
Supervisor
Description
peer-reviewed
Publisher
Univerza v Ljubljani, Fakulteta za Druzbene Vede
Citation
Metodoloski Zvezki;3 (1), pp. 147-162
Collections
Mathematics & Statistics
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Walsh_2006_Alzheimers.pdf
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Keywords
Alzheimer's disease, Bayesian framework
ULRR Identifiers
https://researchrepository.ul.ie/handle/10344/4936
 https://doi.org/10.34961/4190
Funding code
Funding Information
Sustainable Development Goals
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Type
Article
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/
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