Seminar Recording
In this Data Science Under the Hood webinar, Dr Matt Sutton explores Bayesian data science for big data.
About this event
*** This will be an online event only. A Zoom link will be emailed to registrants on the day of the event. ***
Bayesian data science provides a model-based framework that can allow for the incorporation of prior knowledge and appropriate quantification of uncertainty in modelling. Unfortunately, standard Bayesian inference methods suffer a great computational burden when analysing large datasets which causes many practitioners to avoid these methods. In this talk I’ll introduce some new methods designed to make these challenging datasets accessible to Bayesian inference.
Matthew Sutton completed his PhD in 2019 developing new statistical methods to analyse high dimensional data from clinical heath and biological problems. He worked as a postdoc at Lancaster University as part of the multi-university Bayes4Health grant in the United Kingdom. His research interests span broadly across Monte Carlo methods, high dimensional statistics and Bayesian methodology. A particular focus of his current research is in the development of continuous-time Monte Carlo methods for speeding up the computation required for Bayesian inference. He is currently working on the Models and Algorithms research program for the centre.
Details:
Location: | Online |
Start Date: | 28/09/2021 [add to calendar] |
Start Time: | 2pm |
End Date: | 28/09/2021 |
End Time: | 3pm (AEST) |
Register: |