An Algorithmic Introduction to Probability and its Applications

1 credits · Undergraduate

Probabilistic concepts and modeling introduced and explored through developing and implementing computational algorithms. Topics include Probability models, random variables with discrete and continuous distributions, independence, joint distributions, conditional distributions including binomial, multinomial, Gaussian, Poisson. Expectations, functions of random variables, central limit theorem, Poisson Limit theorem, Order Statistics, Bayes' formula, and Markov Chains. Many examples will drawn from algorithms used in modeling and data science. Requires ability to write basic computer programs. Recommended pre/corequisite: Mathematics 218 or 221. Not open to students who have taken Mathematics 228L, 230, or 340, Statistics 230, 231, or 240L.