Beschreibung
These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametric models, Gaussian processes and latent factors models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn how to critically assess how reliably a Bayesian statistical model has been fitted, and how to choose between rival modelling frameworks.
Autorenporträt
Professor Nick Heard received his PhD degree from the Department of Mathematics at Imperial College London in 2001 and currently holds the position of Chair in Statistics at Imperial. His research interests include developing statistical models for cyber-security applications, finding community structure in large dynamic networks, clustering and changepoint analysis, in each case using computational Bayesian methods.
