Introduction to Bayesian Statistics
9:00 am – 4:00 pm MDT
This three-day course will provide an introduction to the theory and methods of Bayesian Statistics. Bayesian Statistics is an emergent area of Statistics applicable to many problems and especially relevant in the context of uncertainty quantification. The course will cover some simple one- and two-parameter problems to provide a flavor for the mechanics of the Bayesian approach. We will then discuss Markov Chain Monte Carlo (MCMC) methods for making inference using more complicated (higher dimensional) models. By the end of the course the partcipants will fit and evaluate models for a dataset of atmospheric CO2 concentrations taken from ice-core measurements. We'll use the open-source statistical software R [https://www.r-project.org/] and the open-source MCMC package that works seamlessly with R called Stan [mc-stan.org/]. The format of the course is hands-on and participants will use their own laptops.
The lead instructor for the course is Alix Gitelman, Professor of Statistics at Oregon State University. She will be assisted by Nathan Lenssen (Columbia University), Pulong Ma (University of Cincinnati), Felipe Tagle (University of Newcastle), Doug Nychka (NCAR/IMAGe), Dorit Hammerling (NCAR/IMAGe) and others. Seats are limited to 12 participants and to apply, please visit the Bayesian Statistics Application link on the left hand-side of the page.