The following pages link to GPML (Q24814):
Displaying 44 items.
- Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming (Q91882) (← links)
- Computationally efficient algorithm for Gaussian process regression in case of structured samples (Q327224) (← links)
- Information-geometric approach to inferring causal directions (Q456729) (← links)
- Fast approximate Bayesian computation for estimating parameters in differential equations (Q517372) (← links)
- Data-driven approximate value iteration with optimality error bound analysis (Q518293) (← links)
- Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm (Q721156) (← links)
- ANOVA Gaussian process modeling for high-dimensional stochastic computational models (Q781984) (← links)
- Reduced order models for many-query subsurface flow applications (Q1693620) (← links)
- Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization (Q1694642) (← links)
- Varying-coefficient models for geospatial transfer learning (Q1698847) (← links)
- Hamiltonian Monte Carlo acceleration using surrogate functions with random bases (Q1703832) (← links)
- Gaussian processes for unconstraining demand (Q1713757) (← links)
- A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions (Q1735988) (← links)
- Data-driven approximate Q-learning stabilization with optimality error bound analysis (Q1737866) (← links)
- A novel kernel regularized nonhomogeneous grey model and its applications (Q2005425) (← links)
- Multi-view Gaussian processes with posterior consistency (Q2056330) (← links)
- Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting (Q2071359) (← links)
- Non-parametric probabilistic load flow using Gaussian process learning (Q2077702) (← links)
- Learning-based vs model-free adaptive control of a MAV under wind gust (Q2101765) (← links)
- Neural-net-induced Gaussian process regression for function approximation and PDE solution (Q2214653) (← links)
- The role of surrogate models in the development of digital twins of dynamic systems (Q2241783) (← links)
- Nonlocal flocking dynamics: learning the fractional order of PDEs from particle simulations (Q2289872) (← links)
- Sequential sensitivity analysis of expensive black-box simulators with metamodelling (Q2308244) (← links)
- Laplace approximation and natural gradient for Gaussian process regression with heteroscedastic Student-\(t\) model (Q2329797) (← links)
- pyGPs -- a Python library for Gaussian process regression and classification (Q2788375) (← links)
- Distinguishing cause from effect using observational data: methods and benchmarks (Q2810806) (← links)
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- Topological sensitivity analysis for systems biology (Q2962261) (← links)
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- Dimension Reduction via Gaussian Ridge Functions (Q4960976) (← links)
- (Q4969101) (← links)
- Physics Information Aided Kriging using Stochastic Simulation Models (Q5015299) (← links)
- AutoDiagnosis: Automatic Data-Driven Configuration of an Automotive Fault Diagnosis Algorithm Using Noisy Two-Stage Optimization (Q5054234) (← links)
- Multidisciplinary robust design optimization under parameter and model uncertainties (Q5059317) (← links)
- The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models (Q5131129) (← links)
- (Q5148929) (← links)
- A Hybrid Framework of Efficient Multi-Objective Optimization of Stiffened Shells with Imperfection (Q5207359) (← links)
- (Q5214184) (← links)
- (Q5249593) (← links)
- (Q5249599) (← links)
- GPflow: a Gaussian process library using TensorFlow (Q5361306) (← links)
- (Q5406030) (← links)
- Kernel discriminant analysis and clustering with parsimonious Gaussian process models (Q5963817) (← links)
