Evaluating probabilities under high-dimensional latent variable models

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 1137-1144

  Murray, Iain ^a   Salakhutdinov, Ruslan ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EQUIVALENCE CLASSES; MARKOV PROCESSES;

DEEP BELIEF NETWORKS; GOLD STANDARDS; HIGH-DIMENSIONAL; HIGHER-DIMENSIONAL; LATENT VARIABLE MODELING; MONTE CARLO ALGORITHMS; PROBABILISTIC BOUNDS; SHORT RUNS; SIMPLE++; TEST SETS;

MONTE CARLO METHODS;

EID: 73249145640     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Ruslan Salakhutdinov and Iain Murray. On the quantitative analysis of Deep Belief Networks. In Proceedings of the International Conference on Machine Learning, volume 25, pages 872-879, 2008.
2. Geoffrey E. Hinton, Simon Osindero, and Yee Whye Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006. (Pubitemid 44024729)
3. Tom Minka. Divergence measures and message passing. TR-2005-173, Microsoft Research, 2005.
4. Michael A. Newton and Adrian E. Raftery. Approximate Bayesian inference with the weighted likelihood bootstrap. Journal of the Royal Statistical Society, Series B (Methodological), 56(1):3-48, 1994.
5. Thomas L. Griffiths, Mark Steyvers, David M. Blei, and Joshua B. Tenenbaum. Integrating topics and syntax. In Advances in Neural Information Processing Systems (NIPS*17). MIT Press, 2005.
6. Hanna M. Wallach. Topic modeling: beyond bag-of-words. In Proceedings of the 23rd international conference on Machine learning, pages 977-984. ACM Press New York, NY, USA, 2006.
7. Radford M. Neal. Annealed importance sampling. Statistics and Computing, 11(2):125-139, 2001. (Pubitemid 33638185)
8. Pierre Del Moral, Arnaud Doucet, and Ajay Jasra. Sequential Monte Carlo samplers. Journal of the Royal Statistical Society B, 68(3):1-26, 2006.
9. Siddhartha Chib. Marginal likelihood from the Gibbs output. Journal of the American Statistical Association, 90(432):1313-1321, December 1995. (Pubitemid 126093144)
10. Christian Ritter and Martin A. Tanner. Facilitating the Gibbs sampler: the Gibbs stopper and the griddy-Gibbs sampler. Journal of the American Statistical Association, 87(419):861-868, 1992.
11. Siddhartha Chib and Ivan Jeliazkov. Marginal likelihood from the Metropolis-Hastings output. Journal of the American Statistical Association, 96(453), 2001.
12. Antonietta Mira and Geoff Nicholls. Bridge estimation of the probability density at a point. Statistica Sinica, 14:603-612, 2004. (Pubitemid 38907079)
13. Francesco Bartolucci, Luisa Scaccia, and Antonietta Mira. Efficient Bayes factor estimation from the reversible jump output. Biometrika, 93(1):41-52, 2006.
14. Radford M. Neal. Erroneous results in "Marginal likelihood from the Gibbs output", 1999. Available from http://www.cs.toronto.edu/-radford/ chib-letter.html.
15. Simon Osindero and Geoffrey Hinton. Modeling image patches with a directed hierarchy of Markov random fields. In Advances in Neural Information Processing Systems (NIPS*20). MIT Press, 2008.
16. Aapo Hyvärinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3):626-634, 1999.
17. Zoubin Ghahramani and Geoffrey E. Hinton. The EM algorithm for mixtures of factor analyzers. Technical Report CRG-TR-96-1, University of Toronto, 1997.
18. Vibhav Gogate, Bozhena Bidyuk, and Rina Dechter. Studies in lower bounding probability of evidence using the Markov inequality. In 23rd Conference on Uncertainty in Artificial Intelligence (UAI), 2007.