Prob Modeling/Stat Computing

0 or 3 Credits · Level: Graduate, Undergraduate

Fundamentals of probability theory: sampling, random variables, independence and conditioning, probability distributions, expected value, law of large numbers, Sampling distributions and central limit theorem. Elements of Monte Carlo methods. Descriptive statistics and graphics, tests, regression, ANOVA, nonparametric methods, linear models, logistic regression. Algorithms used in high dimensional distributions, such as inference using maximum likelihood estimation and Bayesian approaches (expectation maximization, Markov Chain Monte Carlo). If time permits: bootstrapping, density estimation. Prerequisites: Introductory statistics, some coding experience (e.g. R), 3 credits.