Bayesian Inference: An Introduction to Principles and Practice in Machine Learning

By Michael E. Tipping. Advanced Lectures on Machine Learning, 2003

313 citations recorded by the source corpus.

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A review of Bayesian variable selection methods: what, how and which (2009, methodology): “We also omit the machine learning literature focusing on finding maximum a posteriori estimates for parameters (e.g. Tipping 2004).”
Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation (2013, methodology): “…of the data y given hyperparameters θ, as described in Section 2, assuming a zero mean GP. Automatic relevance determination (MacKay et al., 1994; Tipping, 2004) takes place during training, removing extraneous components from the proposed model, through the complexity penalty in the marginal…”
A Primer on Learning in Bayesian Networks for Computational Biology (2007, background): “Tipping’s tutorial [17] contains good illustrations of marginal likelihood, and Ghahramani’s tutorial [18] contains a clear overview introducing structure learning and approximation methods.”
Gaussian Process Kernels for Pattern Discovery and Extrapolation (2013, background): “A type of “automatic relevance determination” (MacKay et al., 1994; Tipping, 2004) takes place during training, minimizing the effect of extraneous components in the proposed model, through the complexity penalty in the marginal likelihood (Rasmussen & Williams, 2006).”