Bayesian analysis is a formal statistical framework for updating knowledge based on observations using the basic laws of probability. Ecologists and social scientists increasingly use Bayesian hierarchal models to deal with complexity in the systems they study. This course provides a principled foundation needed to learn Bayesian approaches to modeling socio-environmental processes.
The National Socio-Environmental Synthesis Center (SESYNC) will host a nine-day short course January 5–14, 2015 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 analytical methods.
The course will enable participants to:
- 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.
- Use basic statistical distributions (e.g., binomial, Poisson, normal, lognormal, multinomial, beta, Dirichlet, gamma) to write joint and conditional posterior distributions for hierarchical Bayesian models that couple models of ecological processes, models of data, and random effects.
- 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.
- Use JAGS software to implement MCMC methods for estimating posterior distributions of parameters, latent states, and derived quantities.
- Evaluate model convergence and assess goodness of fit of models to data.
- Develop and implement hierarchical models that explicitly partition uncertainties.
- Understand the basis for statistical inference from single and multiple Bayesian models.
- Use Bayesian methods to synthesize results from multiple scientific studies.