Why would we want to be Bayesian in the first place? In this workshop we will examine the types of questions which we are able to ask when we view the world through a Bayesian perspective.This workshop will introduce Bayesian approaches to both statistical inference and model based prediction/forecasting. By starting with an examination of the theory behind this school of statistics through a simple example, the participant will then learn why we often need computationally intensive methods for solving Bayesian problems. The participant will also be introduced to the mechanics behind these methods (MCMC), and will apply them in a biologically relevant example.
The participant will: 1) Contrast the underlying philosophies of the Frequentist and Bayesian perspectives.
2) Estimate posterior distributions using Markov Chain Monte Carlo (MCMC).
3) Conduct both inference and prediction using the posterior distribution.
We will build on ideas presented in the workshop on Likelihood Methods. If you did not attend this workshop, it may help to have a look at the slides and script provided on this page.
The goal of this workshop is to demystify the potentially ‘scary‘ topic of Bayesian Statistics, and empower participants (of any preexisting knowledge level) to engage in statistical reasoning when conducting their own research. So come one, come all!
That being said, a basic working understanding of R is assumed. Knowledge of functions and loops in R will be advantageous, but not a must.
This workshop will be conducted entirely in R. We will not be using any external software such as winBUGS.
We will use a package I have written which is available on CRAN: