This workshop will focus on applied Bayesian regression models 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 lecture series begins with a basic introduction to Bayesian analysis and hierarchical linear and generalized linear models. More advanced topics will address common challenges in environmental data analysis including missing data and when the number of observations is too large to efficiently fit the desired hierarchical model. The exercises blend modeling, computing, and data analysis including a hands-on introduction to R, JAGS, and openBUGS. Special attention is given to exploration and visualization of spatial-temporal data and the practical and accessible implementation of spatial-temporal models.