Bayesian Modeling Workshop, May 18-27, 2016, Colorado State University

The National Science Foundation (award #: DEB-1145200) is sponsoring an annual, two-week workshop to provide intensive training in Bayesian modeling for post-doctoral researchers, academic faculty, and agency scientists. Applicants must have earned a PhD to be considered for the workshop, and applications will not be accepted from graduate students. Twenty participants will be invited each year. There will be no cost for participation in the workshop. A $1000 stipend will be provided to each U.S. participant, and a $700 stipend will be provided to each international participant (due to taxes withheld), to defray costs of travel. The fourth workshop will be held May 18-27, 2016 at Colorado State University in Fort Collins, CO. Instructors will include Tom Hobbs, Colorado State University, Mevin Hooten, Colorado State University, Kiona Ogle, Arizona State University, and Maria Uriarte, Columbia University.

Goals of the Workshop
1. Provide a principles-based understanding of Bayesian methods needed to train students, to evaluate papers and proposals, and to solve research problems.
2. Communicate the statistical concepts and vocabulary needed to foster collaboration between ecologists and statisticians.
3. Provide the conceptual foundations and quantitative confidence needed for self-teaching modern analytical methods.

Learning Outcomes: At the end of each workshop participants will be able to:

1. Explain key principles of Bayesian statistics including the concepts of joint, conditional, and marginal probabilities, posterior and prior distributions, likelihood, conjugacy, conditioning, and the relationship among simple Bayesian, hierarchical Bayesian, and maximum likelihood methods.

2. Use basic statistical distributions (e.g., binomial, Poisson, normal, lognormal, multinomial, beta, Dirichlet, gamma) to write joint and conditional posterior distributions for Bayesian models.

3. Explain how Markov chain Monte Carlo (MCMC) methods can be used to estimate the posterior distributions of parameters. Write algorithms and computer code in R implementing MCMC methods to estimate parameters in simple models.

4. Use JAGS software to implement MCMC methods for estimating posterior distributions of parameters, latent states, and derived quantities. Evaluate model convergence. Assess goodness of fit of models to data.

5. Develop and implement hierarchical models that explicitly partition uncertainties.

6. Understand approaches for evaluating the strength of evidence in data for alternative models of ecological processes.

Course Format: The course will include lectures and laboratory exercises. Labs will emphasize problem solving requiring programming in R and JAGS. There will be four to six group projects using data provided by participants. The projects will be aimed at producing published manuscripts.

Prerequisites: Participants must have a general, working knowledge of R.

To Apply: Send a two page curriculum vitae as well as a statement of interest in the course that speaks to the following evaluation criteria:

1. Opportunity to train others in principles and methods learned in the course through teaching or by mentoring research.

2. Opportunity to use principles and methods learned in the course to influence management and policy.

3. Interest in applying course material to their own research.

4. Need for principles based training in Bayesian modeling, and demonstrated lack of opportunities at his/her home institution.

In addition, applicants will be evaluated on their contribution to the diversity of participants, including diversity in sex, age, ethnicity, research interests, geography, and institution.

Deadline: Applications must be received no later than January 15, 2016. Send one PDF file with the CV and qualifications letter as an electronic attachment to Jill Lackett at Please follow the following naming convention for the file: LastName_FirstName_Bayes_2016. Invitations to successful applicants will be sent by February 15, 2016.

Dr. Tom Hobbs
Natural Resource Ecology Lab
Colorado State University

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