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ISBA 2018 Bayesian Foundations Lecture by Judith Rousseau
Asymptotic behaviour of credible regions Judith Rousseau The reknown theorem of Bernstein von Mises in regular finite-dimensional models has numerous interesting consequences, in particular, it implies that a large class of credible regions are also asymptotically confidence regions, which in turns imply that different priors lead to the same credible regions to first order. Unfortunately, […]
Tags: Bayesian Foundations Lectures
June 28, 2018
ISBA 2018 Bayesian Foundations Lecture by Ed George
Bayesian Hospital Mortality Rate Estimation: Calibration and Standardization for Public Reporting Edward I. George Bayesian models are increasingly fit to large administrative data sets and then used to make individualized recommendations. In particular, Medicare’s Hospital Compare webpage provides information to patients about specific hospital mortality rates for a heart attack or Acute Myocardial Infarction (AMI). […]
ISBA 2018 Bayesian Foundations Lecture by Alan Gelfand
Spatial Statistics and Environmental Challenges Alan Gelfand The worlds of spatial statistics and of environmental modeling are both enormous. In a brief one hour lecture, it is not possible to cover much of this terrain. So, I will focus on two large problems which connect both of these areas: modeling of species distributions and modeling […]
ISBA 2018 Bayesian Foundations Lectures by Anthony O’Hagan
In Praise of Subjectivity? Anthony O’Hagan Bayesian analysis requires that probabilities are subjective. Attempts to escape this apparently unwelcome fact are numerous, and they are ultimately misguided because science itself is necessarily subjective. Instead, we should embrace the opportunity to incorporate additional knowledge into the analysis through the prior distribution. But that doesn’t make subjectivity […]
ISBA 2016 Bayesian Foundations Lectures by David Spiegelhalter
Trying to be a ‘public’ (Bayesian) statistician David Spiegelhalter, University of Cambridge (UK) Video Link: http://videolectures.net/isba2016_spiegelhalter_public_statistician/
Tags: Bayesian Foundations Lectures
June 13, 2016
ISBA 2016 Bayesian Foundations Lectures by Sonia Petrone
A subjective tour through foundations and modern trends Sonia Petrone, Universita Bocconi (Italy) This lecture will be a tutorial-tour starting from a brief reminder of the origin of subjective probability and risk, focusing on notions of exchangeability and touching a (personal choice of) problems and current trends. A general question underlies the tour: In the […]
ISBA 2016 Bayesian Foundations Lectures by Peter Green
Graphical modelling and Bayesian structural learning Peter Green, University of Technology, Sydney (UTS), Australia and University of Bristol, UK Conditional independence is key to understanding the structure of multivariate distributions and multivariate data. Graphical modelling provides a rigorous formalism for encoding, visualising and reasoning with conditional independence assumptions, and thus provides tools for assessing structure […]
ISBA 2012 Bayesian Foundations Lecture by Aad van der Vaart
Confidence in nonparametric credible sets? Aad van der Vaart (University of Leiden, Netherlands) In nonparametric statistics, the posterior distribution is used in exactly the same way as in any Bayesian analysis. It supposedly gives us the likelihood of various parameter values given the data. A difference with parametric analysis is that it is often difficult […]
Tags: Bayesian Foundations Lectures
June 25, 2012
ISBA 2012 Bayesian Foundations Lecture by Donald A. Berry
Slowly but surely, Bayesian ideas revolutionize medical research Donald A. Berry (University of Texas M.D. Anderson Cancer Center, USA) Bayesian theory is elegant and intuitive. But elegance may have little value in practical settings. The “Bayesian Revolution” of the last half of the 20th century was irrelevant for biostatisticians. They were busy changing the world […]
ISBA 2012 Bayesian Foundations Lecture by Christian P. Robert
Approximate Bayesian computation (ABC): advances and questions Christian P. Robert (Paris Dauphine University, France) The lack of closed-form likelihoods has been the bane of Bayesian computation for many years and, prior to the introduction of MCMC methods, a strong impediment to the propagation of the Bayesian paradigm. We are now facing models where an MCMC […]
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