“Prior Beliefs, Posterior Distributions and Frequencies – Basic Concepts of Bayesian and Frequentist Statistics” —Speaker: Dr. Ulrike Grittner (Inaugural lecture/Antrittsvorlesung)

Date: Wednesday, January 10th, 2018 /   Time: 4:00pm followed by small reception

*Note, date and location different from usual schedule!

Space is limited, so please register here in advance: https://goo.gl/forms/25HFixnLvJoeMMFI3

Location: Lecture Hall (Hörsaal) of the Neurology Clinic, first floor (Alte Nervenklinik) – follow our signs, Bonhoefferweg 3, Charité Universitätsmedizin Berlin- Campus Mitte, 10117 Berlin

Campus Map:  https://www.charite.de/service/lageplan/plan/map/ccm_bonhoefferweg_3

Description: Bayesian statistics originates from a posthumously published essay written by the English Reverend Thomas Bayes in 1763. Bayes’ essay proposed a new strategy for making statistical inference by combining the best available knowledge at the current time point with results from new data to create new, “better” evidence. In contrast, most statistical techniques used today originate in the early 20th century and are connected to names such as Ronald A. Fisher, Jerzy Neyman, and Karl Pearson. These “frequentist” approaches do not incorporate prior knowledge (prior beliefs) in the inference statistic, but rather make inferences solely using the data at hand. After several decades of heated discussion and debate in the statistical community, the frequentist concepts were long-time favorites, as they were perceived to be more objective. However, in recent times, technological advances have provided new solutions to the challenging numerical problems of Bayesian models, allowing appropriate weights to be given to prior knowledge and the Bayesian approach a chance to catch up. This lecture will provide insights into relevant concepts and applications of both frequentist and Bayesian statistics.

Please feel free to share this invitation. All are welcome. Hope to see you there!