We introduce a graphical factor Analysis Model as graphical Gaussian model with latent variables staisfying a set of conditional indepdndence assumptions. The study of the associations left unexplained by the latent factors allows a better interpretation of the model. A real data example is presented to clarify the ideas. We propose a MCMC method to approximate both the model probabilities and the inference on the quantities of interest.
Graphical Factor Analysis Models: Specification and Model Comparison
STANGHELLINI, Elena
1998
Abstract
We introduce a graphical factor Analysis Model as graphical Gaussian model with latent variables staisfying a set of conditional indepdndence assumptions. The study of the associations left unexplained by the latent factors allows a better interpretation of the model. A real data example is presented to clarify the ideas. We propose a MCMC method to approximate both the model probabilities and the inference on the quantities of interest.File in questo prodotto:
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