In Advances in Neural Information Processing Systems
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Summary
This paper investigates the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for xed values of hyperparameters to be carried out exactly using matrix operations.
- Published
- 1996-01-01
- Cited by
- 6,640
- References
- 10
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2877073
References
- CBMS-NSF REGIONAL CONFERENCE SERIES IN APPLIED MATHEMATICS
- Regularization Theory and Neural Networks Architectures
- Hybrid Monte Carlo
- A Practical Monte Carlo Implementation of Bayesian Learning
- A Practical Bayesian Framework for Backpropagation Networks
- Networks for approximation and learning
- Bayesian Learning via Stochastic Dynamics
- Spline Models for Observational Data
- 5. Statistics for Spatial Data
- Bayesian Methods for Backpropagation Networks
- Bayesian learning for neural networks
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