University of Cambridge > Talks.cam > Machine Learning @ CUED > Variational inference for some models with Polya-Gamma latent variables and Gaussian process priors

Variational inference for some models with Polya-Gamma latent variables and Gaussian process priors

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Dr R.E. Turner.

Polson et al [1] have shown that the logistic sigmoidal function can be represented as a mixture of Gaussians with the Polya-Gamma (PG) density as the mixture distribution. This PG augmentation has attracted considerable interest in the machine learning community. I will discuss a simple variational inference approximation for such models with Gaussian (process) priors and discuss applications to classification, Poisson processes and continuous time Ising models.

[1] N G Polson, J G Scott and J Windle: Bayesian inference for logistic models using Polya-Gamma latent variables; J. Am Stat. Ass. (2015)

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

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2017 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity