There is a Bayesian Cake Club, from where we could find a list of papers on Bayesian Statistics:
- 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
- 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
- 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 inPhylogeny 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
The Annals of Statistics, 4, 1236-9, (1976).
- Belief and the Problem of Ulysses and the Sirens
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
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
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
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)