This course covers basic concepts and methods in applied probability and stochastic modeling. The intended audience is master's and doctoral students in programs such as Computer Science, Statistics, Mathematics, and those in the MS/OM Ph.D. in Booth School of Business. In terms of prerequisites, basic familiarity with probability theory and stochastic processes will be assumed. The exposition will be (mostly) rigorous, yet intentionally skirting some measure-theoretic details; for those interested in such details they can be found in measure theoretic textbooks and other courses (e.g. Measure Theoretic Probability sequence offered by the department of Statistics). A unifying theme in the course will be the use of asymptotic methods which constitute a powerful tool for the study of complex stochastic systems.
Basic familiarity with probability theory and stochastic processes will be assumed.
No provisional grades.
Description and/or course criteria last updated: September 25 2023
