Ecologists are often faced with analyzing relatively complicated data. For example, ecological data sets can be spatially, temporally, or hierarchically structured; they may be missing relevant information; and they likely arise from nonlinear (and non-Gaussian) processes. Additionally, many contemporary problems in ecology require the synthesis of multiple sources and types of data. To accommodate this complexity, Bayesian and hierarchical Bayesian statistical methods are emerging as powerful tools for analyzing such data. This day-long workshop will provide an overview of Bayesian modeling at a relatively introductory level. This includes presentation and discussion of basic concepts, including important elements of Bayesian statistics and hierarchical Bayesian modeling. To complement theses details, we will present case studies that employ Bayesian and/or hierarchical Bayesian analyses, where we focus on the modeling procedure in addition to the ecological problem. We will provide a WinBUGS (Bayesian software package) demo using one of these case studies. During the afternoon, participants will have the opportunity to develop and implement a Bayesian model based on a selection of ecological problems and data that will be provided. By the end of the workshop, participants will be able to understand the fundamentals of Bayesian modeling and develop basic hierarchical models. We will provide reference materials so participants can explore the topics in greater depth. These materials should serve as a jumping-off point for those interested in employing the methods in their own research, or for those who simply want to familiarize themselves with the topic.