Useful priors for covariance operators
Seminar Room 1, Newton Institute
Formulating useful priors for covariance operators is a challenging problem. This problem arises when one wishes to perform Bayesian inference with functional data. We discuss the issues and show that common priors for covariance matrices do not extend to operators on infinite dimensional function spaces. A method for constructing priors is described along with some of the mathematical properties of the priors. We show how to compute with these priors and give an application.