Bayesian Modeling for Socio-Environmental Data 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 hierarchal models provide a powerful approach to analysis of socio-environmental problems that are complex and that require synthesis of knowledge.
Past participants of this short course have worked on research questions including, but not limited to, the use of network analyses to understand measurement uncertainly in the context of extreme weather events, the study of governance effectiveness and fisheries biomass, and the relationship between advocacy group compositions and estuarine quality. The National Socio-Environmental Synthesis Center (SESYNC) will host a nine-day short course August 15 - 25, 2017 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. All participants must be proficient users of R and be able to bring a laptop to each class meeting. Apply for this short course by May 26 on SESYNC's webpage: sesync.us/bayes<http://sesync.us/bayes> Emily S. Cassidy Science Communications Coordinator National Socio-Environmental Synthesis Center (SESYNC) University of Maryland Email: [email protected]<mailto:[email protected]> Phone: 410-919-4990
