Bayesian Modeling

Bayesian Modeling provides methods to evaluate papers and proposals, communicate statistical concepts, and problem solve in research, all of which serve as a foundation for the self-teaching of modern analytical methods.

EACH YEAR A GROUP OF ECOLOGY PROFESSIONALS ATTENDS A 10-DAY INTENSIVE WORKSHOP FOCUSED ON UNDERSTANDING BAYESIAN STATISTICS AND MODELING.

Taught by NREL scientist and ESS faculty member, Tom Hobbs and colleagues, the course provides intensive training for post-docs, university faculty, and agency scientists. The aim of the course is to provide a fundamental foundation in statistical principles needed to understand and use the powerful, flexible Bayesian approach to gaining insight from models using data.

Participants have used the material they learned in the course to develop new graduate level courses at their home institutions, or to modify existing courses, and have used the modelling in publications (journal, policy and management technical reports, monographs, books and book chapters), dissertations, theses, workshops, seminars, presentations, grant proposals, and awards.

Participant feedback from the 2013-2016 workshops showed the most important insights reflected the anticipated learning outcomes of the project which were to


  1. Explain key principles of Bayesian statistics
  2. Use basic statistical distributions in hierarchical Bayesian models
  3. Use Markov Chain Monte Carlo methods
  4. Understand and use JAGS and R software
  5. Develop and implement hierarchical models
  6. Evaluate strength of evidence in alternative models for ecological processes.

Contact Tom Hobbs for more information about Bayesian modeling workshops at Tom.Hobbs@colostate.edu