The following pages link to (Q5405251):
Displaying 20 items.
- Hilbert space methods for reduced-rank Gaussian process regression (Q91877) (← links)
- Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming (Q91882) (← links)
- A comparative evaluation of stochastic-based inference methods for Gaussian process models (Q399908) (← links)
- Nested polynomial trends for the improvement of Gaussian process-based predictors (Q1691892) (← links)
- Stochastic variational hierarchical mixture of sparse Gaussian processes for regression (Q1722733) (← links)
- Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting (Q2071359) (← links)
- Learning ``best'' kernels from data in Gaussian process regression. With application to aerodynamics (Q2083686) (← links)
- Nested aggregation of experts using inducing points for approximated Gaussian process regression (Q2163216) (← links)
- Predictive approaches for choosing hyperparameters in Gaussian processes (Q2747213) (← links)
- Stable and efficient Gaussian process calculations (Q2880909) (← links)
- O(N2)-Operation Approximation of Covariance Matrix Inverse in Gaussian Process Regression Based on Quasi-Newton BFGS Method (Q3447091) (← links)
- (Q4258157) (← links)
- (Q4902124) (← links)
- (Q4969101) (← links)
- Bayesian model calibration for vacuum-ultraviolet photoionisation mass spectrometry (Q5092618) (← links)
- Parametric Approximation Policy Iteration Algorithm Based on Gaussian Process (Q5165995) (← links)
- Understanding Gaussian Process Regression Using the Equivalent Kernel (Q5450963) (← links)
- EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors (Q5858428) (← links)
- A framework of zero-inflated Bayesian negative binomial regression models for spatiotemporal data (Q6076575) (← links)
- A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling (Q6631207) (← links)
