Slide 4
Slide 4 text
a first Bayes 250
Took place in Edinburgh, Sept. 5–7, 2011:
Sparse Nonparametric Bayesian Learning from
Big Data David Dunson, Duke University
Classification Models and Predictions for Ordered
Data Chris Holmes, Oxford University
Bayesian Variable Selection in Markov Mixture
Models Luigi Spezia, Biomathematics
& Statistics Scotland, Aberdeen
Bayesian inference for partially observed Markov
processes, with application to systems biology
Darren Wilkinson, University of Newcastle
Coherent Inference on Distributed Bayesian
Expert Systems Jim Smith, University of Warwick
Probabilistic Programming John Winn, Microsoft
Research
How To Gamble If You Must (courtesy of the
Reverend Bayes) David Spiegelhalter, University
of Cambridge
Inference and computing with decomposable
graphs Peter Green, University of Bristol
Nonparametric Bayesian Models for Sparse
Matrices and Covariances Zoubin Gharamani,
University of Cambridge
Latent Force Models Neil Lawrence, University of
Sheffield
Does Bayes Theorem Work? Michael Goldstein,
Durham University
Bayesian Priors in the Brain Peggy Series,
University of Edinburgh
Approximate Bayesian Computation for model
selection Christian Robert, Universit´
e
Paris-Dauphine
ABC-EP: Expectation Propagation for
Likelihood-free Bayesian Computation Nicholas
Chopin, CREST–ENSAE
Bayes at Edinburgh University - a talk and tour
Dr Andrew Fraser, Honorary Fellow, University of
Edinburgh
Intractable likelihoods and exact approximate
MCMC algorithms Christophe Andrieu,
University of Bristol
Bayesian computational methods for intractable
continuous-time non-Gaussian time series Simon
Godsill, University of Cambridge
Efficient MCMC for Continuous Time Discrete
State Systems Yee Whye Teh, Gatsby
Computational Neuroscience Unit, University
College London
Adaptive Control and Bayesian Inference Carl
Rasmussen, University of Cambridge
Bernstein - von Mises theorem for irregular
statistical models Natalia Bochkina, University of
Edinburgh