Teaching, according to an ancient Chinese scholar, is to propagate truth, impart knowledge, and dispel confusion. I built my teaching philosophy upon my experiences as a lecturer, a guest lecturer and a teaching assistant: good teaching practice involves clear learning outcomes, engagement by active learning, and evaluation/feedback for improvement. A good teacher respects students' independent thinking and helps them establish their own knowledge structure.
The objective of this course is to explore Bayesian statistical methods and discuss their applications in real life problems. Students are expected to have strong background in statistics, probability, and computational methods. Further, they need to be comfortable with at least one program- ming language, e.g. R, Matlab, Python, etc.. By the end of this course, students would learn how to formulate a scientific question by constructing a Bayesian model, and perform Bayesian statistical inference to answer that question. Although the focus of this course is on Bayesian methodology, throughout this course, students would be also exposed to some theoretical aspects of Bayesian inference. They would also learn several advanced computational techniques, and use these techniques for Bayesian analysis of real data.
*: I designed and delivered this 30-hour graduate course spring 2017 at CMS, CalTech.
Course Website: https://eee.uci.edu/17w/37930
*: I was invited to give 3 guest lectures winter 2017 at Dept. of Stats, UC-Irvine.
@2017 Shiwei Lan