Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Statistics and Computing
Nonparametric regression using linear combinations of basis functions
Statistics and Computing
On Bayesian model and variable selection using MCMC
Statistics and Computing
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Bagging for Gaussian process regression
Neurocomputing
A review of Bayesian neural networks with an application to near infrared spectroscopy
IEEE Transactions on Neural Networks
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Gaussian processes have received significant interest for statistical data analysis as a result of the good predictive performance and attractive analytical properties. When developing a Gaussian process regression model with a large number of covariates, the selection of the most informative variables is desired in terms of improved interpretability and prediction accuracy. This paper proposes a Bayesian method, implemented through the Markov chain Monte Carlo sampling, for variable selection. The methodology presented here is applied to the chemometric calibration of near infrared spectrometers, and enhanced predictive performance and model interpretation are achieved when compared with benchmark regression method of partial least squares.