Launch of the Statistics Reading Club

Abstract

The NICDA group will be having a “Statistics Reading Group” to read and discuss technical books in Statistics and Probability. The first book we will read is Anirban DasGupta’s Probability for Statistics and Machine Learning. This reference book of 20 chapters and 784 pages gives a great overview of fundamental and modern probability methodology that is key for statistical inference. Therefore, it is a great resource for PhD students and researchers in Statistics. From its Springer description:

This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.

For the first session, we will start with the basics and target Chapter 1, Review of Univariate Probability. Participants are expected to read the chapter before the session. Then, during the session, we will go page by page over the chapter and discuss its important results, implications, connections, unclear points, etc., producing in the end a small report. Sessions are expected to be held weekly and dynamics are expected to evolve depending on what we see it works best. In this first session Eduardo will lead the discussion.

Everyone interested is welcome to join!