We have collected some relevant papers here to serve as useful background for the workshop.
Theoretical Background
-
A Bayesian Analysis of Some Nonparametric Problems.
Thomas S. Ferguson.
Annals of Statistics, Vol. 1, No. 2, (Mar., 1973).
[jstor] -
Ferguson Distributions Via Polya Urn Schemes.
David Blackwell and James B. MacQueen.
Annals of Statistics, Vol. 1, No. 2, (Mar., 1973).
[jstor] -
Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric
Problems.
Charles E. Antoniak.
Annals of Statistics, Vol. 2, No. 6, (Nov., 1974).
[jstor] -
A Constructive Definition of Dirichlet Priors.
Jayaram Sethuraman.
Statistica Sinica, Vol. 4, (1994).
[statistica sinica]
Statistical Modelling
-
Hierarchical Priors and Mixture Models, With Application in Regression
and Density Estimation.
Mike West, Peter Muller and Michael D. Escobar. Aspects of Uncertainty: A Tribute to D V Lindley, (1994).
[citeseer] -
Bayesian Density Estimation and Inference Using Mixtures.
Michael D. Escobar and Mike West.
Journal of the American Statistical Association, Vol. 90, No. 430, (Jun., 1995).
[jstor]
Sampling Schemes
-
Markov chain sampling methods for Dirichlet process mixture models.
Radford M. Neal.
Technical Report No. 9815, Dept. of Statistics, University of Toronto.
[neal] -
A Split-Merge Markov Chain Monte Carlo Procedure for the Dirichlet Process
Mixture Model.
Sonia Jain and Radford M. Neal.
Technical Report No. 2003, Dept. of Statistics, University of Toronto.
[neal]
Stick Breaking Priors
-
Gibbs sampling methods for stick-breaking priors.
Hemant Ishwaran and Lancelot F. James.
Journal of the American Statistical Association, March 2001, Vol. 96, No. 453, Theory and Methods.
[ishwaran]
Clustering and Infinite Models
-
The Infinite Gaussian Mixture Model.
Carl Edward Rasmussen.
Advances in Neural Information Processing Systems (NIPS) 12, 2000.
[citeseer] -
Defining priors for distributions using Dirichlet diffusion trees.
Radford M. Neal.
Technical Report No. 0104, Dept. of Statistics, University of Toronto, 2001.
[neal]
See also:
Density modeling and clustering using Dirichlet diffusion trees.
Radford M. Neal.
In Bayesian Statistics 7, Oxford University Press, 2003.
[neal] -
The Infinite Hidden Markov Model.
Matthew J. Beal, Zoubin Ghahramani, and Carl Edward Rasmussen.
Advances in Neural Information Processing Systems (NIPS) 14, 2002.
[beal] -
Infinite Mixtures of Gaussian Process Experts.
Carl Edward Rasmussen and Zoubin Ghahramani.
Advances in Neural Information Processing Systems (NIPS) 14, 2002.
[rasmussen] -
Hierarchical Topic Models and the Nested Chinese Restaurant Process.
David M. Blei, Thomas L. Griffiths, Michael I. Jordan and Joshua B. Tenenbaum.
Advances in Neural Information Processing Systems (NIPS) 16, 2004.
[jordan]