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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). […]


June 28, 2018

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 2018 De Finetti Lecture by Philip Dawid

Bruno de Finetti’s Objectivity Philip Dawid While de Finetti famously rejected all objectivist conceptions of Probability, it is less widely understood that he was a strong advocate of objectivity in the assessment of probabilities. In particular, he allowed that one probability forecaster could be better or worse than another, and emphasised the importance of putting […]


June 24, 2018

An Introduction to Bayesian Networks — Philip Dawid

Presented by: Philip Dawid University of Cambridge Date: Monday 26th September 2016 – 10:15 to 11:00 Title: Bayesian networks and argumentation in evidence analysis Abstract: I will outline some basic theory of Bayesian Networks, with forensic applications. Topics will include qualitative and quantitative representation, object-oriented networks, and (time permitting) causal diagrams.


September 26, 2016

ISBA 2016 De Finetti Lecture by Persi Diaconis

Building Apriori Knowledge Into Conclusions Drawn From Simulations Persi Diaconis, Stanford University (United States) Simulations rule much of Bayesian(and nonBayesian) practice. If you look at what most of do with the output of a simulation, it’s surprisingly naive; use the mean +-2 s.d. .What happened to Bayes (or modern statistics)? I have found classes of […]


June 13, 2016

ISBA 2016 Bayesian Foundations Lectures by David Spiegelhalter

Trying to be a ‘public’ (Bayesian) statistician David Spiegelhalter, University of Cambridge (UK) Video Link:


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 […]


June 25, 2012

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