Gaussian processes for bayesian classification via hybrid Monte Carlo

Advances in Neural Information Processing Systems

Volumn , Issue , 1997, Pages 340-346

  Barber, David ^a   Williams, Christopher K I ^a  

^a ASTON UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; GAUSSIAN DISTRIBUTION;

BAYESIAN CLASSIFICATION; BAYESIAN METHODS; GAUSSIAN PROCESSES; HIGH-DIMENSIONAL; HYBRID MONTE CARLO; MARKOV CHAIN MONTE CARLO METHOD; PREDICTION PROBLEM; REAL-WORLD PROBLEM;

GAUSSIAN NOISE (ELECTRONIC);

EID: 0000308194     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. Duane, S., A. D. Kennedy, B. J. Pendleton, and D. Roweth (1987). Hybrid Monte Carlo. Physics Letters B 195, 216-222.
2. Green, P. J.and Silverman, B. W. (1994). Nonparametric regression and generalized liner models. Chapman and Hall.
3. Neal, R. M. (1996). Bayesian Learning for Neural Networks. Springer. Lecture Notes in Statistics 118.
4. Ripley, B. (1996). Pattern Recognition and Neural Networks. Cambridge.
5. Ripley, B. D. (1994). Flexible Non-linear Approaches to Classification. In V. Cherkassy, J. H. Friedman, and H. Wechsler (Eds.), From Statistics to Neural Networks, pp. 105-126. Springer.
6. Sacks, J., W. J. Welch, T. J. Mitchell, and H. P. Wynn (1989). Design and analysis of computer experiments. Statistical Science 4(4), 409-435.
7. Williams, C. K. I. Computing with infinite networks. This volume.
8. Williams, C. K. I. and C. E. Rasmussen (1996). Gaussian processes for regression. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8, pp. 514-520. MIT Press.