This course provides an introduction to probability and parametric inference. Topics include: random variables, standard distributions, the law of large numbers, the central limit theorem, likelihood-based estimation, sampling distributions and hypothesis testing, as well as an introduction to Bayesian methods. Some assignments may involve computation using the R programming language.
- Outcome 1: Students will be able to manipulate random variables and their distributions using differential and integral calculus.
- Outcome 2: Students will be able to derive properties of standard probability.
- Outcome 3: Students will be able to derive maximum likelihood estimators for standard probability distributions and discuss their properties.