scientific article
From MaRDI portal
Publication:2896119
zbMath1242.62098MaRDI QIDQ2896119
Carl Edward Rasmussen, Miguel Lázaro-Gredilla, Joaquin Quiñonero-Candela, Aníbal R. Figueiras-Vidal
Publication date: 13 July 2012
Full work available at URL: http://www.jmlr.org/papers/v11/lazaro-gredilla10a.html
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (31)
Data-driven prognostic model for temperature field in additive manufacturing based on the high-fidelity thermal-fluid flow simulation ⋮ Scalable Inference for Hybrid Bayesian Hidden Markov Model Using Gaussian Process Emission ⋮ Empirical Bayes identification of stationary processes and approximation of Toeplitz spectra ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Variational inference for sparse spectrum Gaussian process regression ⋮ Unnamed Item ⋮ Efficient spatio-temporal Gaussian regression via Kalman filtering ⋮ Online learning‐based model predictive control with Gaussian process models and stability guarantees ⋮ A framework of zero-inflated Bayesian negative binomial regression models for spatiotemporal data ⋮ Deep Gaussian processes for calibration of computer models (with discussion) ⋮ Multi‐fidelity data fusion through parameter space reduction with applications to automotive engineering ⋮ Variational Tobit Gaussian process regression ⋮ Sparse Gaussian processes for solving nonlinear PDEs ⋮ Predicting the Behaviour of the Senescence-Accelerated Mouse (SAM) Strains (SAMPs and SAMR) Using Machine Learning Algorithm ⋮ Real-time model learning using incremental sparse spectrum Gaussian process regression ⋮ Kernel methods in system identification, machine learning and function estimation: a survey ⋮ Multiple instance learning via Gaussian processes ⋮ Error estimation of the parametric non-intrusive reduced order model using machine learning ⋮ Vecchia-approximated Deep Gaussian Processes for Computer Experiments ⋮ Hilbert space methods for reduced-rank Gaussian process regression ⋮ Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming ⋮ Unnamed Item ⋮ Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm ⋮ The matrix ridge approximation: algorithms and applications ⋮ Symmetry exploits for Bayesian cubature methods ⋮ Streaming-Data Selection for Gaussian-Process Modelling ⋮ Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions ⋮ Testing subspace restrictions in the presence of high dimensional nuisance parameters ⋮ Functional models for longitudinal data with covariate dependent smoothness
This page was built for publication:
