Enrollment in the short course as reached it's limit, so registration is closed.
Hierarchical Random Effects Models Using Markov Chain Monte Carlo
Andrew Finley, Michigan State University and Alan Gelfand, Duke University
The one-day short course will explore recent advancements in hierarchical random effects models using Markov chain Monte Carlo methods with application and examples for statistical ecology. The focus is on linear and generalized linear modeling frameworks that accommodate spatial and temporal associations. Lecture and exercises offer an applied perspective on model specification, identifiability of parameters, and computational considerations for Bayesian inference from posterior distributions. The lectures will begin with a basic introduction to Bayesian hierarchical linear models and proceed to address several common challenges in environmental data, including missing data and when the number of observations is too large to efficiently fit the desired hierarchical random effects models. The exercises blend modeling, computing, and data analysis including a hands-on introduction to spBayes package in the R statistical environment. Special attention is given to exploration and visualization of spatial-temporal data and the practical and accessible implementation of spatial-temporal models.
Sunday, 7 September, 2014, with an hour lunch break included at noon.
Registration opens at 8 and the course starts at 9.
Location: Lory Student Center, Room 382