Official description: 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, Bayesâ€™ 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.
Further information: This course:
Questions? Contact Prof. Jonathan Mattingly at jonm@math.duke.edu.
New in Spring 2024.
Not open to students who have taken MATH 228L, MATH 230, MATH 340, STA 230, STA 231, or STA 240L