An Introduction to Bayesian Inference, Methods and Computation

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 nonparametrics, Gaussian processes and latent factor 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 methods for assessing model fit, and to choose between rival modelling approaches.

• Bayesian statistics
• Bayesian Nonparametrics
• computational Bayesian inference
• Monte Carlo
• Stan
• PyStan
• conjugate prior models
• Bayes linear regression
• Bayes Factors
• Gaussian processes
• Bayesian latent factor models

• Imperial College London, London, UK

  Nick Heard

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.

Policies and ethics