Title: Computational Bayes [Lecture 2/3]
Speaker: Aaron Robotham, ICRAR (aaron.robotham AT uwa.edu.au)
Date: 1pm (AEDT) Wednesday 16th October 2013
Abstract: Having established the basic concepts of a Bayesian approach to data analysis, in this lecture we will go further and apply it to more complicated problems that require the use of computers. This will lecture will cover Markov Chain Monte Carlo (MCMC) and more specifically Metropolis Hastings sampling. We will be making use of R in this lecture, and at this stage a basic degree of familiarity is assumed. To get back up to speed the keen student may want to read back through the notes at “Introduction to R” lecture.
Additional Material: Talk slides are available for download here.