SMS scnews item created by Alex Casella at Sun 21 May 2017 1810
Type: Seminar
Distribution: World
Expiry: 20 Aug 2017
Calendar1: 22 May 2017 1700-1800
CalLoc1: Carslaw 535A
CalTitle1: MaPSS: Mathematical Postgraduate Seminar Series
Auth: (acas5565) in SMS-WASM

MaPSS: Mathematical Postgraduate Seminar Series: Sarah Romanes (Sydney University) -- Thinking like a Bayesian - an Introduction to Bayesian Inference

Dear All, 

We are delighted to present the MaPSS Seminar topic of Monday 22/05; please see the
abstract below.  

**This Semester the Seminar will always run on Monday, at 5:00pm in 535A** 

Following the talk, there will be pizza on offer.  

Speaker: Sarah Romanes (Sydney University) 

Title: Thinking like a Bayesian - an Introduction to Bayesian Inference 

Abstract: Almost all of the statistical inference methods learnt at the University of
Sydney concern what is referred to as frequentist inference.  A major alternative to
frequentist inference is Bayesian inference, named after Reverend Thomas Bayes (1701
-1761).  Bayesian inference has many advantages over frequentist inference, including
(but not limited to) allowing for better accounting of uncertainty, and producing
results that are both highly interpretative and intuitive.  

However, Bayesian inference is not without its drawbacks.  Intractable integrals that
appear in Bayesian statistics must be evaluated numerically, and can be quite complex.
The computational complexity of Bayesian statistics has been a major obstacle for its
application in previous years, however with modern computational power Bayesian
approaches to statistical problems are much more feasible and implementable by
researchers.  In this presentation, I will introduce the basic concepts of Bayesian
inference - (including topics such as the posterior, prior choice, and numerical
approximations to Bayesian inferences) in a light-hearted presentation accessible to all
levels of statistical background.  

Supervisors, please encourage your students to attend.  

MaPSS Organizers

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