The last five years have seen the emergence of a powerful new framework for building sophisticated real-world applications based on machine learning. The cornerstones of this approach are (i) the adoption of a Bayesian viewpoint, (ii) the use of graphical models to represent complex probability distributions, and (iii) the development of fast, deterministic inference algorithms, such as variational Bayes and expectation propagation, which provide efficient solutions to inference and learning problems in terms of local message passing algorithms. This paper reviews the key ideas behind this new framework, and highlights some of its major benefits. The framework is illustrated using an example large-scale application.<\/p>\n","protected":false},"excerpt":{"rendered":"
The last five years have seen the emergence of a powerful new framework for building sophisticated real-world applications based on machine learning. The cornerstones of this approach are (i) the adoption of a Bayesian viewpoint, (ii) the use of graphical models to represent complex probability distributions, and (iii) the development of fast, deterministic inference algorithms, 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