MTH2132 Bayesian Inference and Reasoning

This course is an introduction to probability and statistics, with applications to mathematics, science, and engineering. The approach is Bayesian and emphasizes making inferences based on incomplete information. Topics include discrete and continuous probability distributions, conditional probability, prior and posterior probabilities, hypothesis testing, history of the Bayesian approach, and its advantages over the orthodox (frequentist) approach. Applications include p-values and confidence intervals, the Monty Hall problem, code breaking, medical testing, courtroom arguments, and the philosophy of science.



Concurrent Requisites