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Peter Green’s introductory lecture on Bayesian inference can be found here: http://videolectures.net/mlss2011_green_bayesian/. Description: Inference is the process of discovering from data about mechanisms that may have caused or generated that data, or at least explain it. The goals are varied – perhaps simply predicting future data, or more ambitiously drawing conclusions about scientific or societal […]
January 20, 2021
A historical overview of the use of Bayesian inference for categorical data by Alan Agresti [PDF Download]. This is based on a paper which can be found here: [PDF Download].
Aki Nishimura and Antonietta Mira taught a short-course at Lugano about Hamiltonian Monte Carlo (HMC), and here are the source files for the beamer slides with accompanying R codes: https://github.com/aki-nishimura/hmc-lecture!
Tamara Broderick has a wonderful information set (Video, Slides, Code) about Nonparametric Bayes. Check it out here: http://people.csail.mit.edu/tbroderick/tutorial_2016_mlss_cadiz.html!
The ICML 2018 tutorial by Tamara Broderick entitled “Variational Bayes and beyond: Bayesian inference for big data” can be found here: http://people.csail.mit.edu/tbroderick/tutorial_2018_icml.html. Abstract: Bayesian methods exhibit a number of desirable properties for modern data analysis—including (1) coherent quantification of uncertainty, (2) a modular modeling framework able to capture complex phenomena, (3) the ability to incorporate […]
“It Pays to Go Bayes” [Youtube link] is a video lecture series on Bayesian Methods in Econometrics and Forecasting edited by K. Surekha Rao. This series contains twenty-four foundational lectures by prominent Bayesians Arnold Zellner, Jayanta Kumar Gosh, Prem Goel, Wolfgang Wolfgang Polasek, William Griffiths, and a few early Indian Bayesians. An international workshop on […]
January 5, 2021
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, […]
June 28, 2018
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). […]
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 […]
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 […]
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