Abstract

Probability theory is foundational in understanding random phenomena, with the central limit theorem being a cornerstone result. This theorem establishes that the standardized partial sums of independent and identically distributed random variables converge in distribution to a Gaussian random variable. In the 1970s, Charles Stein pioneered a method for proving generalized central limit theorems, with relaxed assumptions to allow for more application scenarios. More importantly, this method allows us to estimate the distance between two probability distributions using differential operators. Since then, many authors in literature have obtained quantitative central limit theorems and found applications in different areas using the so-called Stein’s method. In this talk, we will introduce Stein's method and its applications.

 

WebEx Link:https://nationaltaiwanuniversity-ksz.my.webex.com/nationaltaiwanuniversity-ksz.my/j.php?MTID=md2bebd1dbb0357811d91210c6c9a32f4

Meeting number (access code): 2514 929 6664

Meeting password: enJ3sq233Zd

 

Organizers: Jhih-Huang Li (NTU), Wai Kit Lam (NTU)