Speaker
Anna Scaife
(University of Manchester)
Description
This lecture introduces the machine learning method of Gaussian Process Modelling (GPM). It introduces the concept of covariate Gaussian noise, including the covariance matrix as parameterised using covariance kernels, and illustrates the effect of different degrees of covariance on a simulated dataset. The lecture shows how the covariance matrix can be inverted for imputation of missing data or forward prediction based on a training data set.
