Solutions to pressing environmental problems require understanding connections between human and natural systems. Analysis of these systems requires a model that can deal with complexity, is able to exploit data from multiple sources, and is honest about the uncertainty from multiple sources. Synthesis of results from multiple studies is often required. Bayesian hierarchical models provide a powerful approach to analysis of socio-environmental problems.
Past participants of this short course have worked on research questions including the use of network analyses to understand measurement uncertainly in the context of extreme weather events, the study of governance effectiveness and fisheries biomass, the effect of changing climate on population dynamics of polar bears, and the relationship between advocacy group compositions and estuarine quality.
The National Socio-Environmental Synthesis Center (SESYNC) will host an 11 day short course “Bayesian Modeling for Socio-Environmental Data" from June 3 - June 13, 2019 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 analytic methods.
Short Course Details:
- Applications are due no later than March 15, 2019, at 5 p.m. Eastern Time (ET).
- Selected participants will be notified by April 1, 2019.
- The course is aimed at postdoc, researcher, and faculty participants.
- There is no fee to attend, but participants are responsible for most of their own travel and accommodations.
- Visit sesync.us/bayes2019 for more information and to apply.