This month Dr. Bob Carpenter is visiting from the Applied Statistics Center & Data Science Institute at Columbia University. He will highlight some of the case studies from his forthcoming open-source introductory textbook in probability and statistics. The book assumes only introductory-level math and computation, developing probabilistic modeling and applied Bayesian inference through simulation with fully reproducible R/bookdown code.
In this talk, he'll highlight some of the case studies he is developing
for key concept such as deriving densities as the limit of histograms,
deriving the central limit theorem through simulation, coding and assessing the soundness of random number generators, using simulation
to assess model calibration and sharpness, and the effects of the
(anti-)correlation in Markov chain Monte Carlo draws.
Bob has a Ph.D. in cognitive and computer science (University of Edinburgh), worked as a professor of computational linguistics (Carnegie Mellon University), an industrial researcher and programmer in speech recognition and natural language processing (Bell Labs, SpeechWorks, LingPipe).
As always, pizza and beverages will be provided (thanks SPARK!), and we hope you'll join us afterwards at a to-be-determined bar or restaurant.