This threeday 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 twoparameter 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 icecore measurements. We'll use the opensource statistical software R [https://www.rproject.org/] and the opensource MCMC package that works seamlessly with R called Stan [mcstan.org/]. The format of the course is handson 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 handside of the page.
Monday, July 11, 2016, NCAR Mesa Lab  Damon Room 

9:00  10:15 
Session 1, Introductions and overview 
10:15  10:45 
Break 
10:45  12:00 
Session 2, CO_{2} example, priors (working in R) 
12:00  1:00 
Lunch, on your own, ML Cafeteria is cash only 
1:00  2:15 
Session 3, Picking priors (Brian Reich) 
2:15  2:45 
Break 
2:45  4:00 
Session 4, Introduction to MCMC 
Tuesday, July 12, 2016, NCAR Mesa Lab  Damon Room 

9:00  10:15 
Session 5, Software setup and introduction to nimble 
10:15  10:45 
Break 
10:45  12:00 
Session 6, CO_{2} example (working in R, nimble) 
12:00  1:00 
Lunch, on your own, ML Cafeteria is cash only 
1:00  2:15 
Session 7, Bayesian Model Checking 
2:15  2:45 
Break 
2:45  4:00 
Session 8, Introduction to hierarchical models 
Wednesday, July 13, 2016, NCAR Mesa Lab  Damon Room 

9:00  10:15 
Session 9, Timeseries basics and tools in R and nimble 
10:15  10:45 
Break 
10:45  12:00 
Session 10, Extending the CO_{2 }model in R and nimble 
12:00  1:00 
Lunch, on your own, ML Cafeteria is cash only 
1:00  2:15 
Session 11, Hierarchical Bayesian model inverse problem 
2:15  2:45 
Break 
2:45  4:00 
Session 12, Implementation of inverse CO_{2 }model in R and nimble 