Fundamentals of the Bayesian inference, including the likelihood principle, the discrete version of Bayes theorem, prior and posterior distributions, Bayesian point and interval estimations, and predictions. Bayesian computational methods such as Laplacian approximations and Markov Chain Monte Carlo (MCMC) simulation.
4
UnitsOptional
Grading1, 2, 3
PasstimeNone
Level LimitLetters and science
CollegeLecture
Fantastic Professor. PSTAT 115 is certainly difficult, but the homework and tests are all do-able and he actually applies the concepts to interesting data (like baseball, basketball, and lord of the rings). You will actually enjoy his class.
Professor Franks was by far the most engaging and inspirational professor I've had. Although his homework assignments were challenging, working through them further interested me in the topics of the class. He was very personable and approachable while making lectures fascinating using real-world examples.
Professor Franks is probably the best in the statistics department. He's a great explainer and clearly passionate about the subject. The homeworks are extremely hard and you probably won't finish them if you wait until the last day to start. The tests however are pretty fair and much easier than homeworks.
A king among kings. Franks' knowledge of Bayesian statistics is unending and enchanting. Fun (albeit difficult) homework assignments, fair tests and grading criteria, and a generally pleasant experience. If he's teaching a course, you should be in it.
I'm not sure why Professor Franks doesn't have a page on here. But I made one just to say that he is probably the single best stats professor I've ever had at UCSB. Although his class was a bit challenging, he really made an effort to make it fair and interesting. He is funny, knowledgable, and overall a very engaging professor. 10/10!