The Bayesian Section of the SSA is pleased to announce the following webinar 

Computing Bayes: Bayesian Computation from 1763 to the 21st Century 

with Gael M. Martin 

on 11 November 2020 at 4:00PM (AEDT).         

About this webinar:

The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characterize Bayesian inference, with the rules of probability used to transform prior probability distributions for all unknowns - models, parameters, latent variables - into posterior distributions, subsequent to the observation of data. Conducting Bayesian inference requires the evaluation of integrals in which these probability distributions appear. Bayesian computation is all about evaluating such integrals in the typical case where no analytical solution exists. This paper takes the reader on a chronological tour of Bayesian computation over the past two and a half centuries. Beginning with the one-dimensional integral first confronted by Bayes in 1763, through to recent problems in which the unknowns number in the millions, we place all computational problems into a common framework, and describe all computational methods using a common notation. The aim is to help new researchers in particular - and more generally those interested in adopting a Bayesian approach to empirical work - make sense of the plethora of computational techniques that are now on offer; understand when and why different methods are useful; and see the links that do exist, between them all.

About the presenter:

Gael Martin is a Professor of Econometrics in the Department of Econometrics and Business Statistics at Monash University, Australia, and was an Australian Research Council Future Fellow from 2010 to 2013. Her primary research interests have been in the development of simulation-based inferential and forecasting methods for complex dynamic models in economics and finance. Time series models for long memory, non-Gaussian - including discrete count - data have been a particular focus, with state space representations being central to much of that work. The development of Bayesian simulation-based methods has been a very important part of her research output, including work on the newer computational methods such as approximate Bayesian computation. Her interests centre not only on methods of inference, but on the impact of inferential technique on probabilistic forecasting, and the accuracy thereof. Misspecification of the predictive model has been a particular focus of late, including the development of a `loss-based’ paradigm for prediction that delivers accurate predictions when the predictive model is wrong. She is currently an Associate Editor of Journal of Applied Econometrics, International Journal of Forecasting (IJF) and Econometrics and Statistics, and was a guest editor for a special issue of IJF on Bayesian Forecasting.
Her personal webpage, which includes all published work and some other current projects, is available here.   

Registration

This event is reserved for members of SSA and NZSA. Attendance is free but you will need to register here.  Members of NZSA will need to request the booking code from the NZSA membership officer.

After registration you will receive a Zoom link.

The webinar will be recorded and the recording made available to members on the SSA website in due course.