[ES_JOBS_NET] Summer Short Course on Bayesian Modeling
emilyscassidy at gmail.com
Mon May 22 11:24:47 MDT 2017
*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
· Provide a principles-based understanding of Bayesian methods needed
to train students, evaluate papers and proposals, and solve research
· Communicate the statistical concepts and vocabulary needed to
foster collaboration between ecologists, social scientists, and
· 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
Emily S. Cassidy
Science Communications Coordinator
National Socio-Environmental Synthesis Center (SESYNC)
University of Maryland
Email: ecassidy at sesync.org
Phone: 410-919-4990 <(410)%20919-4990>
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