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Bayesian Inference using Generative Models
Add to Calendar 2023-08-31T19:30:00 2023-08-31T20:30:00 UTC Bayesian Inference using Generative Models 201 Thomas Building
Start DateThu, Aug 31, 2023
3:30 PM
End DateThu, Aug 31, 2023
4:30 PM
Presented By
John C Liechty
Event Series: Statistics Colloquia

Variational Inference (e.g. Variational Bayes) can use a variety of approximating densities. Some recent work has explored using classes of Generative Neural Networks with Jacobians that are either volume preserving or fast to calculate. In this work we explore two points: using more general neural networks, but taking advantage of the conditional independence structure that arises natural in a Hierarchical Bayesian model and a general inference framework, in the Spirit of David Spiegelhalter’s WinBugs software, where a wide range of models can be specified and the software ‘automatically’ generates an approximation of the posterior density.