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Online Model Selection Based on the Variational Bayes
2001
Neural Computation
The Bayesian framework provides a principled way of model selection. This framework estimates a probability distribution over an ensemble of models, and the prediction is done by averaging over the ensemble of models. Accordingly, the uncertainty of the models is taken into account, and complex models with more degrees of freedom are penalized . However, integration over model parameters is often intractable, and some approximation scheme is needed. Recently, a powerful approximation scheme,
doi:10.1162/089976601750265045
fatcat:wzqipqgz6be23nhc7hl3cmc7nu