Workshop: Intensive, interdisciplinary short courses on Bayesian inference for ecologists

Colorado State University, Fort Collins CO

Investigators

Abstract

Progress in environmental biology requires application of statistical methods to gain understanding from models and data. Models describe scientific hypotheses about environmental processes in mathematical terms. Statistics allow researchers to assess the value of their data as evidence supporting or refuting the hypotheses expressed in those models. This procedure is fundamental to all scientific inquiry, but many modern researchers at all stages of their careers lack the training needed to apply it confidently. This award will provide intensive training in Bayesian statistical modeling to graduate students, post-docs, university faculty, and agency researchers. Participants will be actively recruited from groups underrepresented in science. This training will enhance participants’ ability to advance science and to solve complex, environmental problems confronting society and will foster collaborations between ecologists and statisticians. The training workshops will teach basic statistical theory needed to understand Bayesian inference, train participants to write proper mathematical expressions for Bayesian models, enable participants to use Bayesian methods to solve a wide range of analysis problems and disseminate materials to enhance teaching by others. The training will emphasize understanding basic principles of Bayesian modeling using lectures and laboratory exercises during three, annually offered short courses spanning two weeks each. Topics to be covered include probability and distribution theory, Bayes theorem and its relationship to maximum likelihood, writing simple and hierarchical Bayesian models, the Markov chain Monte Carlo algorithm, and model checking. Specialized, advanced topics will be chosen to match the specific interests of course participants. Future collaborations between participants and statisticians will be encouraged by including six statistics graduate students to assist with problem solving during courses. The training will enhance the intellectual capital of the disciplines of environmental biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.