Identifiant pérenne de la notice : 218880537
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Note publique d'information : "The dramatic growth in practical applications for machine learning over the last
ten years has been accompanied by many important developments in the underlying algorithms
and techniques. For example, Bayesian methods have grown from a specialist niche to
become mainstream, while graphical models have emerged as a general framework for
describing and applying probabilistic techniques. The practical applicability of Bayesian
methods has been greatly enhanced by the development of a range of approximate inference
algorithms such as variational Bayes and expectation propagation, while new models
based on kernels have had a significant impact on both algorithms and applications."
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