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Slice sampling with latent Gaussian models

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Many probabilistic models incorporate multivariate Gaussian distributions to explain dependencies between observed variables. Very easy to apply inference algorithms would be useful for rapidly developing and exploring such models. We have developed Elliptical Slice Sampling, a slice-sampling variant that requires zero free parameters and is suitable for updating strongly coupled a-priori Gaussian variates given non-Gaussian observations. We are also developing new robust slice samplers for updating covariance parameters.

This is work with Ryan P. Adams and David J.C. MacKay.

This talk is part of the Machine Learning @ CUED series.

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