
spTimer
 Referenced in 14 articles
[sw24237]
 predicts and temporally forecasts large amounts of spacetime data using [1] Bayesian Gaussian Process ... Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio...

GPML
 Referenced in 32 articles
[sw12890]
 range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean ... ones. Several likelihood functions are supported including Gaussian and heavytailed for regression as well...

RobustGaSP
 Referenced in 6 articles
[sw26272]
 Emulation. Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust ... parameter estimation and prediction using Gaussian stochastic process emulator. See the reference: Mengyang Gu, Xiaojing...

lgcp
 Referenced in 14 articles
[sw22045]
 spatiotemporal prediction and forecasting for logGaussian Cox processes. The main computational tool...

HIBITS
 Referenced in 3 articles
[sw30626]
 binary time series using Gaussian processes with application to predicting sleep states. Motivated ... problem of predicting sleep states, we develop a mixed effects model for binary time series ... stochastic component represented by a Gaussian process. The fixed component captures the effects of covariates ... binaryvalued response. The Gaussian process captures the residual variations in the binary response that...

BayesNSGP
 Referenced in 2 articles
[sw30769]
 methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial ... nimble’ package, and posterior prediction for the Gaussian process at unobserved locations is provided...

GpGp
 Referenced in 1 article
[sw31760]
 Functions for fitting and doing predictions with Gaussian process models using Vecchia’s (1988) approximation...

laGP
 Referenced in 12 articles
[sw14043]
 laGP: Local Approximate Gaussian Process Regression. Performs approximate GP regression for large computer experiments ... based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW...

spTDyn
 Referenced in 2 articles
[sw32493]
 Fits, spatially predicts, and temporally forecasts spacetime data using Gaussian Process (GP): (1) spatially...

MPErK
 Referenced in 2 articles
[sw23610]
 stationary Gaussian Stochastic process models to data from a computer experiment, for predicting the output...

bigGP
 Referenced in 4 articles
[sw23850]
 bigGP: Distributed Gaussian Process Calculations. Distributes Gaussian process calculations across nodes in a distributed memory ... methods for maximum likelihood with normal data, prediction, calculation of uncertainty (i.e., posterior covariance calculations...

GPdoemd
 Referenced in 1 article
[sw26109]
 uses Gaussian process surrogate models to approximate and maximise the divergence between marginal predictive distributions...

acebayes
 Referenced in 8 articles
[sw20243]
 optimisation steps. At each step, a Gaussian process regression model is used to approximate ... goals of parameter estimation, model selection and prediction...

glarma
 Referenced in 5 articles
[sw23274]
 class of observation driven nonlinear nonGaussian state space models. The state vector consists ... autoregressivemoving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial ... conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson...

krisp
 Referenced in 1 article
[sw14717]
 kriging based regression (also known as Gaussian process regression) and optimization of deterministic simulators ... noisy data (correlation kernels, hyperparameter estimation, prediction, cross validation). 2. methods implementing Expected Improvement...

StateSpace.jl
 Referenced in 1 article
[sw29997]
 nonlinear, and the process noise and observation errors may be Gaussian, or from some other ... provide methods to perform the common prediction, filtering, and smoothing tasks for each type...

emulator
 Referenced in 2 articles
[sw25521]
 program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques ... number of undetermined input parameters; many climate prediction models fall into this class. The emulator...

HOSA
 Referenced in 2 articles
[sw21993]
 much more information in a stochastic nonGaussian or deterministic signal than is conveyed ... higherorder spectral analysis capabilities for signal processing applications. The toolbox is an excellent resource ... about concepts and algorithms in statistical signal processing. The HOSA Toolbox is a collection ... adaptive linear prediction are implemented. Also included are algorithms for testing of Gaussianity and Linearity...

GoGP
 Referenced in 1 article
[sw23912]
 based on alternative representation of the Gaussian process under geometric and optimization views, hence termed ... GoGP delivered comparable, or slightly better, predictive performance while achieving a magnitude of computational speedup...

BRECCIA
 Referenced in 1 article
[sw31708]
 numerical models of physics or other processes), and sensor data (as measurements from transducers). Each ... frequency models, algorithmic truncation, floating point roundoff, Gaussian distributions, etc. We propose BRECCIA, a Geospatial ... logical sentences), simulations (e.g., weather or environmental predictions), and sensors (e.g. cameras, weather stations, microphones...