Bayesian Factor Analysis via Concentration

Abstract

We consider factor analysis when we assume the distribution form is known up to its mean and variance. A prior is placed on the mean and variance and then inference is made as to whether or not any latent factors exist. Inference is carried out by comparing the concentrations of the prior and posterior about various subsets of the parameter space that are specified by hypothesizing factor structures. An importance sampling algorithm is developed to handle the case where the prior on the correlation matrix is uniform, independent of the prior on the location and scale parameters.

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Cite this paper

@inproceedings{Cao2010BayesianFA, title={Bayesian Factor Analysis via Concentration}, author={Y . Cao}, year={2010} }