The following pages link to (Q4510985):
Displaying 50 items.
- A prior near-ignorance Gaussian process model for nonparametric regression (Q324678) (← links)
- A comparative evaluation of stochastic-based inference methods for Gaussian process models (Q399908) (← links)
- Bayesian nonparametric estimation of Milky Way parameters using matrix-variate data, in a new Gaussian process based method (Q491403) (← links)
- Fast approximate Bayesian computation for estimating parameters in differential equations (Q517372) (← links)
- Spiked Dirichlet process priors for Gaussian process models (Q544464) (← links)
- Variable selection for nonparametric Gaussian process priors: Models and computational strategies (Q635421) (← links)
- Gaussian process classification: Singly versus doubly stochastic models, and new computational schemes (Q637979) (← links)
- Classification and categorical inputs with treed Gaussian process models (Q649163) (← links)
- Bayesian kernel projections for classification of high dimensional data (Q692966) (← links)
- Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian inverse problems (Q729447) (← links)
- Large data and zero noise limits of graph-based semi-supervised learning algorithms (Q778036) (← links)
- Prediction of non-stationary response functions using a Bayesian composite Gaussian process (Q829711) (← links)
- Laplace approximation for logistic Gaussian process density estimation and regression (Q899031) (← links)
- Elicitation of multivariate prior distributions: a nonparametric Bayesian approach (Q963850) (← links)
- Sampling latent states for high-dimensional non-linear state space models with the embedded HMM method (Q1631578) (← links)
- Hamiltonian Monte Carlo acceleration using surrogate functions with random bases (Q1703832) (← links)
- Efficient strategy for the Markov chain Monte Carlo in high-dimension with heavy-tailed target probability distribution (Q1750100) (← links)
- Locally adaptive smoothing with Markov random fields and shrinkage priors (Q1752016) (← links)
- Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities (Q1940028) (← links)
- Emulating dynamic non-linear simulators using Gaussian processes (Q2002727) (← links)
- Locally induced Gaussian processes for large-scale simulation experiments (Q2058747) (← links)
- Large-scale local surrogate modeling of stochastic simulation experiments (Q2157538) (← links)
- Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (Q2179219) (← links)
- Efficient Bayesian shape-restricted function estimation with constrained Gaussian process priors (Q2195831) (← links)
- Random-effects meta-analysis of phase I dose-finding studies using stochastic process priors (Q2233150) (← links)
- A statistical pipeline for identifying physical features that differentiate classes of 3D shapes (Q2245140) (← links)
- Combining feature spaces for classification (Q2270739) (← links)
- Non-stationary phase of the MALA algorithm (Q2315120) (← links)
- Kernel-based mixture models for classification (Q2354731) (← links)
- Bayesian nonparametric regression with varying residual density (Q2434133) (← links)
- Flexible link functions in nonparametric binary regression with Gaussian process priors (Q2827177) (← links)
- A flexible cure rate model for spatially correlated survival data based on generalized extreme value distribution and Gaussian process priors (Q2829470) (← links)
- A Gaussian process regression approach to a single-index model (Q3021173) (← links)
- Uncertainty Quantification in Graph-Based Classification of High Dimensional Data (Q3176234) (← links)
- Learning in the Absence of Training Data—A Galactic Application (Q3297244) (← links)
- (Q4258157) (← links)
- Ergodicity of Markov chain Monte Carlo with reversible proposal (Q4684877) (← links)
- (Q5011565) (← links)
- Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy (Q5052906) (← links)
- Scalable Computation of Predictive Probabilities in Probit Models with Gaussian Process Priors (Q5057082) (← links)
- MCMC Algorithms for Posteriors on Matrix Spaces (Q5057083) (← links)
- Adaptive multiple importance sampling for Gaussian processes (Q5106877) (← links)
- Randomized Algorithms for Lexicographic Inference (Q5126607) (← links)
- Understanding Gaussian Process Regression Using the Equivalent Kernel (Q5450963) (← links)
- Cross-Validation--based Adaptive Sampling for Gaussian Process Models (Q5862906) (← links)
- Model-based transductive learning of the kernel matrix (Q5898261) (← links)
- Model-based transductive learning of the kernel matrix (Q5920613) (← links)
- Analytical uncertainty quantification approach based on adaptive generalized <scp>co‐Gaussian</scp> process model (Q6092235) (← links)
- Estimation of region of attraction with Gaussian process classification (Q6092467) (← links)
- A singular woodbury and pseudo-determinant matrix identities and application to Gaussian process regression (Q6105986) (← links)
