Course content

Statistical analysis involves first setting up a model for data in terms of certain unknown parameters. Bayesian analysis proceeds by placing a prior distribution on these parameters and then deriving and using relevant aspects of the consequent posterior distribution. Bayesian nonparametrics is the extended branch of such modelling and analyses where the parameter of the model is of very high or infinite dimension, as when one models an unknown density, regression, or link function.  This calls for more complex mathematics and computational schemes than for the classical cases where the parameter is of low dimension. There are links to and implications for machine learning.