• tgp

  • Referenced in 33 articles [sw07921]
  • Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps...
  • GPML

  • Referenced in 32 articles [sw12890]
  • wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified ... functions are supported including Gaussian and heavy-tailed for regression as well as others suitable...
  • Kernlab

  • Referenced in 81 articles [sw07926]
  • classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab includes ... Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver...
  • invGauss

  • Referenced in 112 articles [sw11207]
  • invGauss: Threshold regression that fits the (randomized drift) inverse Gaussian distribution to survival data. invGauss ... fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described ... Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based...
  • George

  • Referenced in 15 articles [sw29786]
  • flexible Python library for Gaussian Process (GP) Regression. A full introduction to the theory...
  • laGP

  • Referenced in 12 articles [sw14043]
  • laGP: Local Approximate Gaussian Process Regression. Performs approximate GP regression for large computer experiments...
  • SPOT

  • Referenced in 74 articles [sw06347]
  • includes methods for tuning based on classical regression and analysis of variance techniques; tree-based ... such as CART and random forest; Gaussian process models (Kriging), and combinations of di erent...
  • acebayes

  • Referenced in 8 articles [sw20243]
  • optimisation steps. At each step, a Gaussian process regression model is used to approximate...
  • spTimer

  • Referenced in 15 articles [sw24237]
  • using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models...
  • gprege

  • Referenced in 6 articles [sw31093]
  • expressed gene expression time-courses through Gaussian process regression”. The software fits two GPs with...
  • gptk

  • Referenced in 5 articles [sw24250]
  • implements a general-purpose toolkit for Gaussian process regression with a variety of covariance functions...
  • OP-ELM

  • Referenced in 21 articles [sw12171]
  • detail and then applied to several regression and classification problems. Results for both computational time ... machine (SVM), and Gaussian process (GP). As the experiments for both regression and classification illustrate...
  • GPareto

  • Referenced in 3 articles [sw28711]
  • Pareto Front Estimation and Optimization. Gaussian process regression models, a.k.a. Kriging models, are applied...
  • muq

  • Referenced in 2 articles [sw33064]
  • internally and through NLOPT); Regression (including Gaussian process regression...
  • lineqGPR

  • Referenced in 1 article [sw31421]
  • package lineqGPR: Gaussian Process Regression Models with Linear Inequality Constraints. Gaussian processes regression models with...
  • GPLP

  • Referenced in 1 article [sw08483]
  • local and parallel computation toolbox for Gaussian process regression. This paper presents the getting-started ... local and parallel computation toolbox for Gaussian process regression (GPLP), an open source software package...
  • GoGP

  • Referenced in 1 article [sw23912]
  • GoGP: Fast Online Regression with Gaussian Processes ... most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets ... paper, we introduce a novel online Gaussian process model that could scale with massive datasets ... based on alternative representation of the Gaussian process under geometric and optimization views, hence termed...
  • krisp

  • Referenced in 1 article [sw14717]
  • kriging based regression (also known as Gaussian process regression) and optimization of deterministic simulators...
  • pyGPS

  • Referenced in 1 article [sw14301]
  • pyGPS -- a python library for Gaussian process regression and classification. We introduce pyGPs, an object...
  • mvdens

  • Referenced in 1 article [sw22349]
  • estimation, Gaussian mixtures, vine copulas and Gaussian process regression; and we test whether they ... Additionally, both the density estimation and the regression methods can be used to obtain ... marginal likelihood. In low dimensionality, Gaussian processes are most accurate, whereas in higher dimensionality Gaussian...