{"success":1,"msg":"","color":"rgb(28, 35, 49)","title":"<b>Bayesian Uncertainty<\/b>","description":"webinar","title2":"","start":"2021-03-22 16:00","end":"2021-03-22 17:00","responsable":"<a href=\"mailto:Daniel.kowal@rice.edu\">Dan Kowal <i class=\"fa fa-envelope\"><\/i><\/a>","speaker":"Stephen G. Walker","id":"33","type":"webinar","timezone":"America\/Chicago","activity":"Register in advance for this meeting here: https:\/\/riceuniversity.zoom.us\/meeting\/register\/tJEucuuvrT4jHdSqfa4nXARKCZaTUfB3Imaw","abstract":"Frequentist uncertainty is well understood in terms of the estimation of a sampling distribution of a statistic, acknowledging that the observed finite sample was one of many possible available. On the other hand, Bayesian uncertainty appears to start with a prior distribution and makes no acknowledgement of the variability of the data.\r\n\r\nThe talk aims to shed light on Bayesian uncertainty and indeed shows how the posterior distribution can be understood through variability in the data. We show how it can lead to practical implementations of this interpretation of the Bayesian approach and illustrations will be presented.\r\n\r\nJoint work with Edwin Fong (Turing Institute) and Chris Holmes (Oxford)."}