This workshop explores recent advancements in hierarchical random effects models using Markov chain Monte Carlo (MCMC) methods. 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 workshop begins with a basic introduction to Bayesian hierarchical linear models and proceeds 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 workshop will blend modeling, computing, and data analysis including a hands-on introduction to 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.