This is a course for everyone who want to start learning Bayesian Inference. I hope I can help you understand Bayesian inference as much as I would like to.
Are you ready?
Let's go!
The course is divided in two parts combining theoretical and practical aspects, and the idea is to teach it in two hours.
PART I: Introduction to Bayesian inference. History of Bayes theorem. Bayes theorem. Bayesian inference. Posterior distribution. Credible intervals. Predictive distribution.
PART II: Hierarchical Bayesian models. Definition. Computation. MCMC methods. Metropolis Hastings. An application to disease mapping.
To take full advantage of the course, it is necessary that everyone has the following programs installed:
This will be the packages required for the course
install.packages(pkgs = c("ggplot2", "gridExtra", "maptools", "rgdal", "spdep", "lattice", "latticeExtra", "viridis", "splancs", "lattice", "fields", "plotKML", "raster", "sp", "R2OpenBUGS", "LearnBayes", "dplyr", "coda"))