|Bayesian statistics, Philosophy and Practice
|Prof Daniel Williamson
Since the 1980s, computational advances and novel algorithms have seen Bayesian methods explode in popularity, today underpinning modern techniques in data analytics, pattern recognition and machine learning as well as numerous inferential procedures used across science, social science and the humanities. This module will introduce Bayesian statistical inference, describing the differences between it and classical approaches to statistics. It will develop the 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 based techniques for inference. As well as underpinning a philosophical understanding of Bayesian reasoning with theory, we will use software currently used for Bayesian inference in the lab, allowing you to apply techniques discussed in the course to real data. Pre-requisite: MTH2006 Statistical Modelling and Inference or equivalent
Please note that all modules are subject to change, please get in touch if you have any questions about this module.