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. Thanks, MaPSS Organizers