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SUMMARY:Bayesian Inference using Generative Models - John Liechty (Pennsyl
 vania State University)
DTSTART:20230609T130000Z
DTEND:20230609T140000Z
UID:TALK199498@talks.cam.ac.uk
CONTACT:Qingyuan Zhao
DESCRIPTION:Variational Inference (e.g. Variational Bayes) can use a varie
 ty of approximating densities. Some recent work has explored using classes
  of Generative Neural Networks with Jacobians that are either volume prese
 rving or fast to calculate. In this work we explore two points: using more
  general neural networks\, but taking advantage of the conditional density
  structure that arises naturally in a Hierarchical Bayesian model and a ge
 neral inference framework\, in the Spirit of David Spiegelhalter’s WinBu
 gs software\, where a wide range of models can be specified and the softwa
 re ‘automatically’ generates an approximation of the posterior density
 .
LOCATION:MR11/B1.39\, Centre for Mathematical Sciences
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