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  • libagf
  • Referenced in 5 articles [sw05808]
  • Adaptive Gaussian Filtering is a simple and powerful implementation of variable bandwidth kernel estimators...

  • GaussianProcesses.jl
  • Referenced in 2 articles [sw42274]
  • user-friendly Gaussian processes package. The package provides many mean and kernel functions with supporting...

  • MLMOD
  • Referenced in 1 article [sw39711]
  • model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. We discuss...

  • mvdens
  • Referenced in 1 article [sw22349]
  • evaluate the accuracy of kernel density estimation, Gaussian mixtures, vine copulas and Gaussian process regression...

  • FastGaSP
  • Referenced in 2 articles [sw28204]
  • exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward...

  • meanShiftR
  • Referenced in 1 article [sw32946]
  • neighbor implementations for the Gaussian, Epanechnikov, and biweight product kernels...

  • FMGaBP
  • Referenced in 1 article [sw16762]
  • Parallel finite element technique using Gaussian belief propagation. The computational efficiency of Finite Element Methods ... performance of sparse algebraic kernels. The introduced FEM Multigrid Gaussian Belief Propagation (FMGaBP) algorithm...

  • kde2d
  • Referenced in 1 article [sw17578]
  • diagonal bandwidth matrix. The kernel is assumed to be Gaussian. The two bandwidth parameters...

  • GPyTorch
  • Referenced in 15 articles [sw35483]
  • scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize ... efficient matrix-matrix multiplication with the kernel and its derivative. In addition, BBMM uses...

  • MLatom
  • Referenced in 0 articles [sw40218]
  • kernel ridge regression and supports Gaussian, Laplacian, and Matérn kernels. It can use arbitrary, user...

  • MCMC
  • Referenced in 8 articles [sw03488]
  • Gaussian likelihood and prior. In case of Gaussian error model, sample the model error variance ... timeseries plots, 2 dimensional clouds of points, kernel densities, and histograms. Calculate densities, cumulative distributions...

  • kpcalg
  • Referenced in 1 article [sw26711]
  • version of PC algorithm that uses kernel based independence criteria in order to be able ... deal with non-linear relationships and non-Gaussian noise...

  • BOCK
  • Referenced in 5 articles [sw32130]
  • propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea is to transform the ball ... transformation. Because of the transformed geometry, the Gaussian Process-based surrogate model spends less budget...

  • ICV
  • Referenced in 1 article [sw35488]
  • Kernel Density Estimation. Functions for computing the global and local Gaussian density estimates based...

  • naivebayes
  • Referenced in 1 article [sw35211]
  • Gaussian, Poisson and non-parametric representation of the class conditional density estimated via Kernel Density...

  • Conake
  • Referenced in 0 articles [sw15663]
  • four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation...

  • QPdecon
  • Referenced in 1 article [sw41736]
  • problem, especially for typical noise distributions like Gaussian. We develop a density deconvolution estimator based ... that can achieve better estimation than kernel density deconvolution methods. The QP approach appears...

  • RKHSMetaMod
  • Referenced in 1 article [sw28872]
  • optimization problem. In the context of the Gaussian regression model, RKHSMetaMod estimates a meta model ... Sparse Optimization Problem based on the Reproducing Kernel Hilbert Spaces (RKHS). The estimated meta model...

  • krisp
  • Referenced in 1 article [sw14717]
  • implements kriging based regression (also known as Gaussian process regression) and optimization of deterministic simulators ... model for deterministic or noisy data (correlation kernels, hyper-parameter estimation, prediction, cross validation...

  • mad-GP
  • Referenced in 1 article [sw42206]
  • more easily explore the design space of Gaussian process (GP) surrogate models for modeling potential ... prior guess for the PES) and kernel function (i.e., a constraint on the class...