Breadcrumb
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 homemade cake at every meeting!
Next Meeting
Friday 24 May, at 12pm, 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 email reminders of the upcoming meetings and paper,
please email 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 TwoGroups 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 Highthroughput 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), 271281.

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), 329352

Constructing summary statistics for approximate Bayesian computation: semiautomatic 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 
Likelihoodfree Estimation of model evidence
by Xavier Didelot, Richard G. Everitt, Adam M. Johansen and Daniel J. Lawson 
On the use of nonlocal 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 Sparsityinducing Priors
Anthony Lee, Francois Caron, Arnaud Doucet, Chris Holmes 
Dynamics of Bayesian updating with dependent data and misspecified models
Cosma Rohilla Shalizi 
Posterior Predictive pvalues in Bayesian Hierarchical Models
G.H. Steinbakk, G.O. Storvik, Scandinavian Journal of Statistics, Vol. 36: 320336, 2009, doi: 10.1111/j.14679469.2008.00630.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. 382401. 
Optimal Predictive Model Selection
Barbieri and Berger (2004), The Annals of Statistics, 32, 870897. 
Use of Exchangeability
JFC Kingman (1978), The Annals of Probability, 6, 183197. 
The concept of exchangeability and its applications
Bernardo (1996). 
Hybrid Dirichlet mixture models for functional data
Petrone, Guindani and Gelfand, JRSSB, 71, 755782 (2009).
Some notes from Peter to facilitate reading the above: cribsheet 
Reducing the Dimensionality of Data with Neural Networks
Hinton and Salakhutdinov, Science 313, 504507, 2006.
Supplementary material: tech rep, slides . 
Joint Bayesian Estimation of Alignment and Phylogeny
BENJAMIN D. REDELINGS AND MARC A. SUCHARD, Syst. Biol. 54, 401418, 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, 125135, (2008). 
Agreeing to Disagree
Aumann
The Annals of Statistics, 4, 12369, (1976). 
Belief and the Problem of Ulysses and the Sirens
Van Fraassen
Philosophical Studies, 77, 737 (1995) 
Updating Subjective Probability
Diaconis and Zabell
JASA, 77, 380, 822830 (1982) 
Objective Bayesian variable selection.
G. Casella and E. Moreno
JASA, 101, 157167 (2006). 
Separation measures and the geometry of Bayes factor selection for classification.
J.Q. Smith, P.E. Anderson, and S. Liverani
JRSSB, 70, 5, 957980 (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. 12721317 (1993) 
Subjective Bayesian Analysis: Principles and Practice
M. Goldstein
Bayesian Analysis, Vol. 1, 403420 (+discussion), 2006 
Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models
O. Papaspiliopoulos and G. O. Roberts
Biometrika, Vol. 95, pp. 169186 (2008) 
Bayesian calibration of computer models
M.C. Kennedy and A. O'Hagan
Journal of the Royal Statistical Society, Series B, Volume 63, pp. 425464 (2001) 
Multiplebias 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. 267306 (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. 953963 (1991). 
Causal Inference Without Counterfactuals
A. P. Dawid
Journal of the American Statistical Association, Vol. 95, pp. 407424 (2000) 
Extended Ensemble Monte Carlo
Y. Iba
Int. J. Mod. Phys. C12, 623656 (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): 196212. 
P Values for Composite Null Models
M. J. Bayarri and James O. Berger
JASA, 95 (452), 11271142 (2000). 
Gibbs Sampling Methods for StickBreaking Priors
H. Ishwaran and L. F. James
JASA, 96 (453), 161173 (2001) 
Bayesian density regression
Dunson, D., Pillai, N., and Park J.H.
JRSS(B) 69(2), 163183, 2007. 
Bayesian Inference for Causal Effects: The Role of Randomization
D. B. Rubin
Annals of Statistics, Vol. 6, No. 1, pp 3458 (1978)