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Bayesian feature and model selection for Gaussian mixture models [PDF]

open access: yesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of a mixture model formulation that takes into account the saliency of the features and a Bayesian approach to mixture learning that can be used to estimate the number of ...
Aristidis Likas   +1 more
exaly   +5 more sources
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Bayesian Post-Model-Selection Estimation

IEEE Signal Processing Letters, 2021
Estimation after model selection refers to the problem where the exact observation model is unknown and is assumed to belong to a set of candidate models. Thus, a data-based model-selection stage is performed prior to the parameter estimation stage, which affects the performance of the subsequent estimation.
Nadav Harel, Tirza Routtenberg
openaire   +1 more source

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