ARC Grant Success Funding 2017-2023

Fast approximate inference methods:

new algorithms, applications and theory

Funding: $386,828 AUD

Proposal IDDP180100597

 

Administering Organisation University of Technology Sydney

 

Investigators:

  1. Prof Matt Wand (Chief Investigator)

  2. Prof. Gareth Peters (Partner Investigator)

 

Proposal Summary:

This project aims to develop new algorithms and theory for fast approximate inference and lay down infrastructure to aid future extensions. Fast approximate inference methods are a principled and extensible means of fitting large and complex statistical models to big data sets. They come into their own in applications where speed is paramount and traditional approaches are not feasible. The project aims to lead to practical outcomes from better business decision-making for insurance data warehouses, to improved medical imaging technology.

QRSLab since 2009

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