Creator: J.J. Garcia-Luna-Aceves
This course offers an introduction to fundamental tools of stochastic analysis. Probability, conditional probability; Bayes’ Theorem; random variables and transforms; independence; Bernnoulli trials; statistics, inference from limited data; outcomes of repeated experiments; applications to design; assessment of relative frequency and probability; law of large numbers; precision of measurements; elements of stochastic processes, Poisson processes; Markov chains. Students cannot receive credit for this course and Applied Mathematics and Statistics 131.
General education code(s): SR
Prerequisites: course 16 or 16H and Mathematics 22 or 23A
Type of course and Credit: Hybrid, 5 units
Course first offered: Spring 2019