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

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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.

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