Variational inference for Bayesian mixtures of factor analysers

Advances in Neural Information Processing Systems

Volumn , Issue , 2000, Pages 449-455

  Ghahramani, Zoubin ^a   Beai, Matthew J ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

MIXTURES; OPTIMIZATION;

BAYESIAN INTEGRATION; NUMBER OF COMPONENTS; POSTERIOR DISTRIBUTIONS; PREDICTIVE DENSITY; STOCHASTIC PROCEDURE; UNBIASED ESTIMATES; VARIATIONAL APPROXIMATION; VARIATIONAL INFERENCE;

APPROXIMATION ALGORITHMS;

EID: 84898934543     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. H. Attias. Inferring parameters and structure of latent variable models by variational Bayes. In Proc. 15th Conf. on Uncertainty in Artificial Intelligence, 1999.
2. CM. Bishop. Variational PCA. In Proc. Ninth Int. Conf. on Artificial Neural Networks. ICANN, 1999.
3. Z. Ghahrarnani. H. Attias, and M.J. Beai. Learning model structure. Technical Report GCNU-TR-1999-006, (in prep.) Gatsby Unit, Univ. College London, 1999.
4. Z. Ghahrarnani and GB. Hinton. The EM algorithm for mixtures of factor analyzers. Technical Report CRG-TR-96-1 [http://www.gatsby.ucl.ac.uk/~zoubin/ papers/tr-96-l.ps.gz], Dept. of Comp. Sci., Univ. of Toronto, 1996.
5. D.J.C. MacKay. Ensemble learning for hidden Markov models. Technical report, Cavendish Laboratory, University of Cambridge, 1997.
6. R.M. Neal. Assessing relevance determination methods using DELVE. In CM. Bishop, editor, Neural Networks and Machine Learning, 97-129. Springer-Verlag, 1998.
7. CE. Rasmussen. The infinite gaussian mixture model. In Adv. Neur. Inf. Proc. Sys. 12. MIT Press, 2000.
8. S. Richardson and P.J. Green. On Bayesian analysis of mixtures with an unknown number of components. J. Roy. Stat. Soc.-Ser. B, 59(4):731-758, 1997.
9. S.J. Roberts, D. Husmeier, I. Rezek, and W. Penny. Bayesian approaches to Gaussian mixture modeling. IEEE PAMI, 20(11):1133-1142, 1998.
10. S. T. Roweis and Z. Ghahrarnani. A unifying review of linear Gaussian models. Neural Computation, 11(2):305-345, 1999.
11. M.E. Tipping and CM. Bishop. Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2):443-482, 1999.
12. N. Ueda, R. Nakano, Z. Ghahrarnani, and G.E. Hinton. SMEM algorithm for mixture models. In Adv. Neur. Inf. Proc. Sys. 11. MIT Press, 1999.
13. S. Waterhouse, D.J.C. Mackay, and T. Robinson. Bayesian methods for mixtures of experts. In Adv. Neur. Inf. Proc. Sys. 7. MIT Press, 1995.