Hierarchical Dirichlet Processes

Durbin r eddy s krogh a and mitchison g 1998

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Durbin, R., Eddy, S., Krogh, A., and Mitchison, G. (1998), Biological Sequence Analysis , Cam- bridge, UK: Cambridge University Press. Escobar, M. and West, M. (1995), “Bayesian Density Estimation and Inference Using Mixtures,” Journal of the American Statistical Association , 90, pp. 577–588. Ferguson, T. (1973), “A Bayesian Analysis of Some Nonparametric Problems,” Annals of Statistics , 1(2), pp. 209–230. Fong, D., Pammer, S., Arnold, S., and Bolton, G. (2002), “Reanalyzing Ultimatum Bargaining— Comparing Nondecreasing Curves Without Shape Constraints,” Journal of Business and Eco- nomic Statistics , 20, pp. 423–440. Forsyth, D. A. and Ponce, J. (2002), Computer Vision—A Modern Approach , Upper Saddle River, NJ: Prentice-Hall. Fraley, C. and Raftery, A. E. (2002), “Model-Based Clustering, Discriminant Analysis, and Density Estimation,” Journal of the American Statistical Association , 97, pp. 611–631. Gabriel, S. et al. (2002), “The Structure of Haplotype Blocks in the Human Genome,” Science , 296, pp. 2225–2229. Green, P. and Richardson, S. (2001), “Modelling Heterogeneity with and without the Dirichlet Process,” Scandinavian Journal of Statistics , 28, pp. 355–377. Huang, X., Acero, A., and Hon, H.-W. (2001), Spoken Language Processing , Upper Saddle River, NJ: Prentice-Hall. Ishwaran, H. and James, L. (2004), “Computational Methods for Multiplicative Intensity Models using Weighted Gamma Processes: Proportional Hazards, Marked Point Processes and Panel Count Data,” Journal of the American Statistical Association , 99, pp. 175–190. Ishwaran, H. and Zarepour, M. (2002), “Exact and Approximate Sum-Representations for the Dirichlet Process,” Canadian Journal of Statistics , 30, pp. 269–283. Jain, S. and Neal, R. (2000), “A Split-Merge Markov Chain Monte Carlo Procedure for the Dirichlet Process Mixture Model,” Tech. Rep. 2003, Department of Statistics, University of Toronto. Kleinman, K. and Ibrahim, J. (1998), “A Semi-parametric Bayesian Approach to Generalized Linear Mixed Models,” Statistics in Medicine , 17, pp. 2579–2596. MacEachern, S. (1999), “Dependent Nonparametric Processes,” in Proceedings of the Section on Bayesian Statistical Science , American Statistical Association. 28
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MacEachern, S., Kottas, A., and Gelfand, A. (2001), “Spatial Nonparametric Bayesian Models,” Tech. Rep. 01-10, Institute of Statistics and Decision Sciences, Duke University. MacEachern, S. and M¨uller, P. (1998), “Estimating Mixture of Dirichlet Process Models,” Journal of Computational and Graphical Statistics , 7, pp. 223–238. MacKay, D. and Peto, L. (1994), “A Hierarchical Dirichlet Language Model,” Natural Language Engineering . MacKay, D. J. C. (1997), “Ensemble Learning for Hidden Markov Models,” Tech. rep., Cavendish Laboratory, University of Cambridge. Mallick, B. and Walker, S. (1997), “Combining Information from Several Experiments with Non- parametric Priors,” Biometrika , 84, pp. 697–706. Muliere, P. and Petrone, S. (1993), “A Bayesian Predictive Approach to Sequential Search for an Optimal Dose: Parametric and Nonparametric Models,” Journal of the Italian Statistical Society , 2, pp. 349–364.
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