The aim of the course is to introduce life scientists to Bayesian statistics. We will explore basic ideas regarding integration through simulation (Monte Carlo integration), the philosophy and strengths of Bayesian statistics, and the Markov Chain Monte Carlo (MCMC) algorithms needed to fit such models.
Credits: 3
Prerequisites:
• Conceptual understanding of integrals
• The student should be comfortable programming in R (e.g., be comfortable creating and
manipulating vectors and matrices, creating loops and your own functions, creating queries using Boolean logic, etc.)
• STA6166, STA6093, or a similar introductory statistics course are highly recommended.
• It is highly recommended for students to have had a previous course on mathematical statistics (e.g., “ZOO6927 Statistical Principles for the Biological Sciences” by Jose Ponciano; “STA 5325 Fundamentals of Probability”; or “Foundations of Probability & Math Statistics: a scientific computing approach” by Nikolay Bliznyuk).
