An Applied Introduction to Bayesian Statistics for Ecologists (hosted by TWS)

TWS Workshop

Advance Registration Required

Bayesian methods are increasingly being used to analyze wildlife and fisheries data and have some advantages over classical (frequentist) methods that many ecologists use. The aim of this workshop is to introduce participants to Bayesian statistics, with the expectation that participants will become better-equipped consumers and producers of ecological inference based on Bayesian methods of data analysis. The workshop focus will be on applied tutorials built around simple and familiar statistical models; and discussions of the underlying philosophy, computational methods, and mathematical theory will be brief and motivated by real-world examples. Participants will use Bayesian methods to calculate basic summary statistics (mean, proportion), then advance to fitting regression models commonly used in ecological inference (linear regression, generalized linear model [GLM]). Hands-on exercises will be run using free, open-source software (Program R and JAGS); and participants are expected to bring a laptop computer with software-installation permissions. The intended audience is students/professionals generally familiar with regression models but with absolutely no experience with Bayesian methods. Participants familiar with fitting and interpreting non-Bayesian regression models in Program R (functions lm and glm) will likely benefit most. The content and exercises will rely heavily on two textbooks (Kéry 2010 – Introduction to WinBUGS for Ecologists; Kruschke 2015 – Doing Bayesian Data Analysis), but neither textbook is required to participate. We expect that participants will leave with an increased fluency to evaluate research based on Bayesian statistics and with an increased capacity to learn and apply Bayesian methods in their own ecological research.
Organizers: Jason Carlisle, Trent McDonald
Supported by: Western EcoSystems Technology, Inc.

TWS Workshop
Location: Reno-Sparks CC Date: September 29, 2019 Time: 8:00 am - 12:00 pm