Bayesian Inference for Extended Latent Gaussian Models

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Event details

Date and time 20.11.2020 16:1518:00  
Place and room
zoom
Online https://epfl.zoom.us/j/81419572433
Speaker Alex Stringer, University of Toronto
Category Conferences - Seminars

We define a novel class of additive regression models called Extended Latent Gaussian Models and derive inference methodology for this class which scales to large datasets and is accompanied by theoretical guarantees.
We demonstrate this approach on several examples including spatial downscaling, spatial proportional hazards regression with partial likelihood, and Gaussian Process classification, and demonstrate improved performance and scalability for existing Bayesian approaches to Generalized Linear Mixed and Additive Models.
A notable feature of the method is the natural manner in which we account for uncertainty in “smoothness” parameters, a well-known and difficult problem which we address in this talk through both application and theory.
 

Practical information

  • Informed public
  • Free

Organizer

  • Prof. Anthony Davison

Contact

  • Maroussia Schaffner

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