University of Bristol, Department of Mathematics

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Bayesian Cake Club

The Bayesian Cake Club is a (very informal) reading group of the Statistics group. At the start of each academic year, we roughly agree on what papers we want to read throughout the year. These can be of very different type and are usually loosely related to the Bayesian paradigm: some are more applied, others more theoretical, sometimes philosophical, sometimes algorithmic etc. It often takes more than one meeting to read through and discuss one paper. We meet roughly every fortnight in term time.
The particular attraction of the Bayesian Cake Club (apart from the lively discussions) is of course that we have a home-made cake at every meeting!

Next Meeting

Friday 24 May, at 1-2pm, SM3.
We will continue to read
Bayesian methods in extreme value modelling: a review and new developments by Coles and Powell (1996), ISR

Join the Club

Anyone can join the Bayesian Cake Club. Postgrad students are particularly welcome and encouraged to attend! If you want to receive e-mail reminders of the upcoming meetings and paper, please e-mail Vanessa Didelez.

Papers Read in 2012/13

• Shihao Ji et al (2008): Bayesian Compressive Sensing , IEEE Transactions on Signal Processing
• Frank and Goodman (2012): Predicting Pragmatic Reasoning in Language Games, Science
• Candes and Wakin (2008) An Introduction to Compressive Sampling. IEEE Signal Processing Magazine.
• Efron (2008). Microarrays, Empirical Bayes and the Two-Groups Model. Statistical Science.
• Casella (1985): An Introduction to Empirical Bayes. The Am. Statistician.
• Baggerly and Coombes (2009): Deriving Chemosensitivity from Cell Lines: Forensic Bioinformatics and Reproducibility Research in High-throughput Biology. Annals of Applied Statistics.

Past Papers

• A.Philip Dawid (1994), Selection Paradoxes of Bayesian Inference.
• Persi Diaconis (1977), Finite Forms of de Finetti's Theorem on Exchangeability, Synthese, 36(2), 271-281.
• You May Believe You Are a Bayesian But You Are Probably Wrong
  by Stephen Senn,
• B.T. Knapik A.W. van der Vaart J.H. van Zanten (2011) Bayesian Inverse problems with Gaussian Priors.
• C. Yau and C. Holmes. (2011) Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination. Bayesian Analysis 6(2), 329-352
• Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation
  by Fearnhead and Prangle
• Philosophy and the practice of Bayesian statistics
  by Gelman and Shalizi
• Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the Akaike information criterion - Bayesian information criterion dilemma
  by van Erven, Grunwald and de Rooij
• Suboptimal behaviour of Bayes and MDL in classification under misspecification
  by Peter Grunwald and John Langford
• Likelihood-free Estimation of model evidence
  by Xavier Didelot, Richard G. Everitt, Adam M. Johansen and Daniel J. Lawson
• On the use of non-local prior densities in Bayesian hypothesis tests
  by Valen E. Johnson and David Rossell
• Approximate Bayesian Computation: A Nonparametric Perspective
  by Michael Blum
• Inconsistent Bayesian Estimation
  by Christensen
• A Hierarchical Bayesian Framework for Constructing Sparsity-inducing Priors
  Anthony Lee, Francois Caron, Arnaud Doucet, Chris Holmes
• Dynamics of Bayesian updating with dependent data and misspecified models
  Cosma Rohilla Shalizi
• Posterior Predictive p-values in Bayesian Hierarchical Models
  G.H. Steinbakk, G.O. Storvik, Scandinavian Journal of Statistics, Vol. 36: 320-336, 2009, doi: 10.1111/j.1467-9469.2008.00630.x
• Bayesian Model Averaging: A Tutorial
  Jennifer A. Hoeting, David Madigan, Adrian E. Raftery and Chris T. Volinsky, Statistical Science, Vol. 14, No. 4 (Nov., 1999), pp. 382-401.
• Optimal Predictive Model Selection
  Barbieri and Berger (2004), The Annals of Statistics, 32, 870-897.
• Use of Exchangeability
  JFC Kingman (1978), The Annals of Probability, 6, 183-197.
• The concept of exchangeability and its applications
  Bernardo (1996).
• Hybrid Dirichlet mixture models for functional data
  Petrone, Guindani and Gelfand, JRSSB, 71, 755-782 (2009).
  Some notes from Peter to facilitate reading the above: cribsheet
• Reducing the Dimensionality of Data with Neural Networks
  Hinton and Salakhutdinov, Science 313, 504-507, 2006.
  Supplementary material: tech rep, slides .
• Joint Bayesian Estimation of Alignment and Phylogeny
  BENJAMIN D. REDELINGS AND MARC A. SUCHARD, Syst. Biol. 54, 401-418, 2005.
  (Some introductory background reading on Phylogeny can be found in Phylogeny Estimation: Traditional and Bayesian Approaches by M. Holder and P.O. Lewis, Nature Reviews, 2003.)
• Bayesian inference for a discretely observed stochastic kinetic model
  Boys, Wilkinson and Kirkwood.
  Stat Comput, 18, 125-135, (2008).
• Agreeing to Disagree
  Aumann
  The Annals of Statistics, 4, 1236-9, (1976).
• Belief and the Problem of Ulysses and the Sirens
  Van Fraassen
  Philosophical Studies, 77, 7-37 (1995)
• Updating Subjective Probability
  Diaconis and Zabell
  JASA, 77, 380, 822-830 (1982)
• Objective Bayesian variable selection.
  G. Casella and E. Moreno
  JASA, 101, 157-167 (2006).
• Separation measures and the geometry of Bayes factor selection for classification.
  J.Q. Smith, P.E. Anderson, and S. Liverani
  JRSSB, 70, 5, 957-980 (2008).
• Examples of Adaptive MCMC
  Roberts, G. O. and Rosenthal, J. S.; Preprint (2008)
• Hyper Markov laws in the statistical analysis of decomposable graphical models
  S. Lauritzen and P. Dawid
  Annals of Statistics, Vol. 21, pp. 1272-1317 (1993)
• Subjective Bayesian Analysis: Principles and Practice
  M. Goldstein
  Bayesian Analysis, Vol. 1, 403-420 (+discussion), 2006
• Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models
  O. Papaspiliopoulos and G. O. Roberts
  Biometrika, Vol. 95, pp. 169-186 (2008)
• Bayesian calibration of computer models
  M.C. Kennedy and A. O'Hagan
  Journal of the Royal Statistical Society, Series B, Volume 63, pp. 425-464 (2001)
• Multiple-bias modelling for analysis of observational data
  S. Greenland
  Journal of the Royal Statistical Society: Series A (Statistics in Society), Volume 168, Number 2, March 2005 , pp. 267-306 (2005)
• Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments
  C. Currin, T. Mitchell, M. Morris, and D. Ylvisaker
  Journal of the American Statistical Association, v. 86, pp. 953-963 (1991).
• Causal Inference Without Counterfactuals
  A. P. Dawid
  Journal of the American Statistical Association, Vol. 95, pp. 407-424 (2000)
• Extended Ensemble Monte Carlo
  Y. Iba
  Int. J. Mod. Phys. C12, 623-656 (2001)
• Sparse graphical models for exploring gene expression data
  A. Dobra, B. Jones, C. Hans, J.R. Nevins and M. West.
  Journal of Multivariate Analysis, 90 (2004): 196-212.
• P Values for Composite Null Models
  M. J. Bayarri and James O. Berger
  JASA, 95 (452), 1127-1142 (2000).
• Gibbs Sampling Methods for Stick-Breaking Priors
  H. Ishwaran and L. F. James
  JASA, 96 (453), 161-173 (2001)
• Bayesian density regression
  Dunson, D., Pillai, N., and Park J.-H.
  JRSS(B) 69(2), 163-183, 2007.
• Bayesian Inference for Causal Effects: The Role of Randomization
  D. B. Rubin
  Annals of Statistics, Vol. 6, No. 1, pp 34-58 (1978)