modules

Since the 1980s, computational advances and novel algorithms have seen Bayesian methods explode in popularity, today underpinning modern techniques in data science and machine learning with applications across science, social science, the humanities and finance.

This module will introduce Bayesian statistics and reasoning. It will develop the philosophical and mathematical ideas of subjective probability theory for decision-making and explore the place subjectivity has in scientific reasoning. It will develop Bayesian methods for data analysis and introduce modern Bayesian simulation, including Markov Chain Monte Carlo and Hamiltonian Monte Carlo. The course balances philosophy, theory, mathematical calculation and analysis of real data ensuring the student is equipped to use Bayesian methods in future jobs aligned to data analysis and to take Bayesian research projects.

Pre-requisites: A basic introduction to probability and to classical statistics, plus experience of a programming language for data science such as R or Python. A preliminary online refresher course covering some basics in probability, integration and likelihood theory, supported by the module leader, is given alongside the first 2 weeks of the module to ensure students have the required knowledge to complete the course.

_{Please note that all modules are subject to change, please get in touch if you have any questions about this module.}