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Abstract: . Gaussian processes are a natural way of defining prior distributions over functions of one or more input variables. In a simple nonparametric regression problem, where such a function gives the mean of a Gaussian distribution for an observed response, a Gaussian process model can easily be implemented using matrix computations that are feasible for datasets of up to about a thousand cases. Hyperparameters that define the covariance function of the Gaussian process can be sampled using Markov... (Update)
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BibTeX entry: (Update)
Neal, R. M. (1997) Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Technical Report CRG--TR--97--2, Dept. of Computer Science, University of Toronto. http://citeseer.ist.psu.edu/neal97monte.html More
@misc{ neal97monte,
author = "R. Neal",
title = "Monte Carlo implementation of Gaussian process models for Bayesian regression
and classification",
text = "Neal, R. M. (1997) Monte Carlo implementation of Gaussian process models
for Bayesian regression and classification. Technical Report CRG--TR--97--2,
Dept. of Computer Science, University of Toronto.",
year = "1997",
url = "citeseer.ist.psu.edu/neal97monte.html" }
Citations (may not include all citations):269 Bayesian Learning for Neural Networks (context) - Neal - 1996 ACM
258 Matrix Analysis (context) - Horn, Johnson - 1985 ACM
199 Probabilistic Inference Using Markov Chain Monte Carlo Metho.. - Neal - 1993
91 Adaptive rejection sampling for Gibbs sampling (context) - Gilks, Wild - 1992
84 Hybrid Monte Carlo (context) - Duane, Kennedy et al. - 1987 ACM
78 Gaussian processes for regression - Williams, Rasmussen - 1996 DBLP
53 Evaluation of Gaussian Processes and other Methods for Non-L.. - Rasmussen - 1996 ACM
42 Elements of Statistical Computing (context) - Thisted - 1988 ACM
41 Correlation Theory of Stationary and Related Random Function.. (context) - Yaglom - 1987
21 Gaussian processes for Bayesian classification via hybrid Mo.. - Barber, Williams - 1997 DBLP
17 Maximum likelihood estimation of models for residual covaria.. (context) - Mardia, Marshall - 1984
17 Variational Gaussian process classifiers - Gibbs, MacKay
17 Efficient implementation of Gaussian processes - Gibbs, MacKay
12 A generalized guided Monte Carlo algorithm (context) - Horowitz - 1991
11 Bayesian Inference (context) - O'Hagan - 1994
10 Improper priors, spline smoothing and the problem of guardin.. (context) - Wahba - 1978
8 Curve fitting and optimal design for prediction (context) - O'Hagan - 1978
2 An improved acceptance procedure for the hybrid Monte Carlo .. (context) - Postscript, http et al. - 1994 ACM
1 Design and analysis of computer experiments (context) - Postscript, http et al. - 1989
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.cs.toronto.edu/~radford/papers-online.html): More
Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered .. - Neal (1995) (Correct)
Bayesian Training of Backpropagation Networks by the Hybrid Monte.. - Neal (1993) (Correct)
Annealed Importance Sampling - Neal (1998) (Correct)
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