MTH2132 Bayesian Inference and Reasoning

This course may be used to satisfy the Probability and Statistics requirement

This course is an introduction to probability and statistics, with applications to mathematics, science, and engineering. The approach is Bayesian and emphasizes making decisions based on incomplete information. Topics include discrete and continuous probability distributions, conditional probability, prior and posterior probabilities, hypothesis testing, Shannon information, decision making, history of the Bayesian approach, and its advantages over the orthodox (frequentist) approach. Applications include: p values and confidence intervals, statistical mechanics and entropy, the Monty Hall problem, code breaking, plausible reasoning in mathematics, how Laplace estimated the mass of Saturn, and playing games of imperfect information such as blackjack or Mastermind.



Concurrent Requisites