bayesian-inference-for-biologists

Welcome

1 Welcome

These notes accompany BIOL 7950: Bayesian Inference for Biologists at Kennesaw State University.

The goal of this course is to introduce Bayesian statistical thinking and modern Bayesian analysis for biological data.

1.1 Course organization

The book is organized into four parts:

1. Foundations: introduces the basic ideas behind Bayesian inference, Bayes’ rule, and how Bayesian inference differs from frequentist inference.
2. Bayesian methods in R: introduces some key aspects of working with Bayesian models in R and JAGS.
3. Bayesian analyses in R and JAGS: demonstrates Bayesian versions of common models for biology like linear models, GLMs, and nonlinear models.
4. Special applications: demonstrates some situations where Bayesian methods really shine, like mark-recapture and state space models.

This book is always a work in progress and I welcome constructive criticism and feedback. You can get in touch with me by email, on Bluesky ([at]greenquanteco), and Twitter ([at]GreenQuantEco).

Visit the lab website for more information about Dr. Green’s other courses and QuantEco lab research!