Solutions to pressing environmental problems require understanding connections between human and natural systems. Analysis of these systems requires models that can deal with complexity, are able to exploit data from multiple sources, and are honest about uncertainty that arises in different ways. Synthesis of results from multiple studies is often required. Bayesian hierarchical models provide a powerful approach to analysis of socio-environmental problems that are complex and that require synthesis of knowledge.
The National Socio-Environmental Synthesis Center (SESYNC) will host a nine-day short course August 19–28, 2015 covering basic principles of using Bayesian models to gain insight from data. The goals of the course are to:
- Provide a principles-based understanding of Bayesian methods needed to train students, evaluate papers and proposals, and solve research problems.
- Communicate the statistical concepts and vocabulary needed to foster collaboration between ecologists, social scientists, and statisticians.
- Provide the conceptual foundations and quantitative confidence needed for self-teaching modern analytical methods.