The infinite Gaussian mixture model

Advances in Neural Information Processing Systems

Volumn , Issue , 2000, Pages 554-559

  Rasmussen, Carl Edward ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN MIXTURE MODEL; GIBBS SAMPLING; INFINITE GAUSSIAN MIXTURE MODELS; MIXTURE COMPONENTS; NUMBER OF COMPONENTS;

MARKOV PROCESSES;

EID: 79955803023     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

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2. Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. Annals of Statistics 1, 209-230.
3. Gilks, W. R. and P. Wild (1992). Adaptive rejection sampling for Gibbs sampling. Applied Statistics 41, 337-348.
4. Neal, R. M. (1996). Bayesian Learning for Neural Networks, Lecture Notes in Statistics No. 118, New York: Springer-Verlag.
5. Neal, R. M. (1998). Markov chain sampling methods for Dirichlet process mixture models. Technical Report 4915, Department of Statistics, University of Toronto. http://www.cs.toronto.edu/~radford/mixmc.abstract.html.
6. Richardson, S. and P. Green (1997). On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society, B 59, 731-792.
7. Ueda, N., R. Nakano, Z. Ghahramani and G. E. Hinton (1998). SMEM Algorithm for Mixture Models, NIPS 11, MIT Press.
8. West, M., P. Müller and M. D. Escobar (1994). Hierarchical priors and mixture models with applications in regression and density estimation. In P. R. Freeman and A. F. M. Smith (editors), Aspects of Uncertainty, pp. 363-386. John Wiley.
9. Williams, C. K. I. and C. E. Rasmussen (1996). Gaussian Processes for Regression, in D. S. Touret-zky, M. C. Mozer and M. E. Hasselmo (editors), NIPS 8, MIT Press.