Covers discrete-time and continuous-time Markov chains, Poisson point processes, and renewal processes, with applications to queueing, branching, and models in science, engineering, and business.
Timo Seppalainen made complicated probability topics accessible, even for students without an analysis background. He explained why mathematical results were important and how they fit into broader contexts. Students found the homework challenging, noting it was harder than the exams, but valued the clarity of his lectures.
Recent recorded grades — Spring 2015: 3.42 GPA, 53.8% A/AB (n=13 letter grades); Spring 2019: 3.87 GPA, 92.6% A/AB (n=27 letter grades); Spring 2025: 3.96 GPA, 96.4% A/AB (n=28 letter grades).