• 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...
  • vbmp

  • Referenced in 2 articles [sw35354]
  • Regression. Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior...
  • celerite

  • Referenced in 2 articles [sw29785]
  • celerite provides fast and scalable Gaussian Process (GP) Regression in one dimension and is implemented...
  • vbdcast

  • Referenced in 1 article [sw34857]
  • relies on flexible nonparametric nonlinear Gaussian process (GP) regression fits that “memorize” the trajectories...
  • MPErK

  • Referenced in 2 articles [sw23610]
  • used to fit regression plus stationary Gaussian Stochastic process models to data from a computer...
  • GPFDA

  • Referenced in 7 articles [sw14770]
  • package GPFDA: Gaussian Process for Functional Data Analysis. Functionalities for modelling functional data with multidimensional ... function-valued processes. In addition, there are functionalities for functional regression models where the mean...
  • spate

  • Referenced in 6 articles [sw12265]
  • package then provide parameterizations of the process part of the model as well as computationally ... MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass ... included in the model through a regression term...
  • joineRML

  • Referenced in 4 articles [sw19419]
  • modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal ... captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo...
  • AMIDST

  • Referenced in 5 articles [sw21741]
  • computing clusters managed by modern big data processing tools like Apache Spark or Apache Flink ... factor analysis, mixture of Gaussians and linear regression models). Our approach compares favorably to stochastic ... variables using a computer cluster with 128 processing units (AWS). The proposed methods are released...
  • BRISC

  • Referenced in 1 article [sw31761]
  • using BRISC. Fits Bootstrap with univariate spatial regression models using Bootstrap for Rapid Inference ... large datasets using Nearest Neighbor Gaussian Processes detailed in Saha and Datta...
  • HIBITS

  • Referenced in 4 articles [sw30626]
  • binary-valued response. The Gaussian process captures the residual variations in the binary response that ... rates over existing approaches such as logistic regression, generalized additive mixed model, models for ordinal...
  • glarma

  • Referenced in 5 articles [sw23274]
  • Gaussian state space models. The state vector consists of a linear regression component plus ... conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson...
  • STK

  • Referenced in 2 articles [sw23922]
  • parametric Bayesian method using a Gaussian Process (GP) prior. The STK also provides tools ... such as Geostatistics, Machine Learning, Non-parametric Regression...
  • KernelToolBox

  • Referenced in 1 article [sw15844]
  • Processing ToolBox for MATLAB. Kernel Regression-Based Image Processing ToolBox is a command-line based ... several general image processing tasks; for instance, 1) Image denoising: Gaussian noise removal; Compression artifact...
  • BFDA

  • Referenced in 2 articles [sw14769]
  • Gaussian process for functional data and mean function, and the assumption of Inverse-Whishart process ... option of approximating the Bayesian inference process with cubic B-spline basis functions is integrated ... scale functional data. Examples of functional data regression with one functional independent variable, scalar...
  • lrgs

  • Referenced in 0 articles [sw16144]
  • regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic ... either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base...
  • LISA

  • Referenced in 1 article [sw28388]
  • contrasting sequences of images, the image processing in LISA starts with selecting a region ... noises an image using a mixture of Gaussian distributions of the pixel intensity values, while ... contrasting images based on a mid-line regression whereas manual registration lines up the images ... formed by two user-selected points. The processed images are then rendered for simultaneous statistical...
  • DPWeibull

  • Referenced in 0 articles [sw20438]
  • dependent Dirichlet process model is used for regression data. The package is designed to handle ... competing risks. Inside each cluster of Dirichlet process, we assume a multiplicative effect of covariates ... dimensional data with Dirichlet mixture of Gaussian distributions...
  • ARfit

  • Referenced in 38 articles [sw00046]
  • ARfit is a collection of Matlab modules for...
  • ATLAS

  • Referenced in 197 articles [sw00056]
  • This paper describes the Automatically Tuned Linear Algebra...