[PDF] Top 20 Additive Kernels for Gaussian Process Modeling
Has 10000 "Additive Kernels for Gaussian Process Modeling" found on our website. Below are the top 20 most common "Additive Kernels for Gaussian Process Modeling".
Additive Kernels for Gaussian Process Modeling
... simulator for a limited number of inputs called the Design of Experiments ...as Gaussian Process modeling ...of Gaussian Processes ...distributions for the response values at any ... Voir le document complet
18
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data
... framework for the spatio-temporal analysis of large-scale collections of multi-modal brain ...accounting for the uncertainty of the temporal profiles and brain structures we wish to ...trajectory ... Voir le document complet
52
A design methodology for additive manufacturing applied to fused deposition modeling process
... time. For example, the creation of functional surfaces from the constraints of the specifi- cations, the generation of the assembly of these surfaces and the verification of volumes of work could be done by the ... Voir le document complet
18
Group kernels for Gaussian process metamodels with categorical inputs
... Our contributions on group kernels are now listed. We exploit the hierarchy group/level by revisiting a nested Bayesian linear model where the response term is a sum of a group effect and a level effect. The level ... Voir le document complet
35
Environmental performance modeling for additive manufacturing processes
... 3.2. Additive manufacturing and environment : state of the art In additive manufacturing, parts are obtained with a successive addition of ...deposition modeling machines based on Eco-Indicator 95 ... Voir le document complet
9
On ANOVA Decompositions of Kernels and Gaussian Random Field Paths
... new kernels from old with KANOVA While kernel methods and Gaussian process modelling have proven efficient in a number of classification and prediction problems, finding a suitable kernel for ... Voir le document complet
15
Gaussian Process Modelling under Inequality Constraints
... algorithm for the automatic knot insertion using an evolution criterion based on the maximisation of the integrated squared error of the MAP ...considered additive (and block-additive) ...considering ... Voir le document complet
219
An efficient methodology for modeling complex computer codes with Gaussian processes
... However, for its application to complex industrial problems, developing a robust implementation methodology is ...the Gaussian process ...and for small size samples (a few ... Voir le document complet
30
Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila
... tested for the inference of mRNA ...is. For further discussions, we refer to GP-mRNA and GP-Protein to the physically- inspired GP with prior over the mRNA or protein concentrations, ... Voir le document complet
17
A Gaussian Process Regression Model for Distribution Inputs
... obtain Gaussian processes indexed by probability ...results for these ...studied kernels, compared to more standard kernels operating on finite dimensional projections of the ...the ... Voir le document complet
19
On ANOVA decompositions of kernels and Gaussian random field paths
... and Gaussian process modelling have proven efficient in a number of classification and prediction problems, finding a suitable kernel for a given application is often judged ...stationary ... Voir le document complet
16
On the stopping criterion for numerical methods for linear systems with additive Gaussian noise
... However, for large-scale prob- lems, the full sequential process can prove prohibitively costly in terms of ...methods for symmetric definite positive linear systems, such as the conjugate gradient ... Voir le document complet
20
Receiver study for cooperative communications in convolved additive α-stable interference plus Gaussian thermal noise
... holds for the same stability index members, in general the additive convolution of the impulsive stable interference and lighter tailed Gaussian thermal noise will not result in a stable ...Inverse ... Voir le document complet
6
Gaussian process regression with linear inequality constraints
... which Gaussian process regression is one of the most popular ...framework for incorporating any type of linear constraints in Gaussian process modeling, including common bound ... Voir le document complet
24
Probabilistic modeling of heteroscedastic laboratory experiments using Gaussian process regression
... to Gaussian Process Regression for creating probabilistic models from few replicated specimens displaying a heteroscedastic ...model for the permeability in order to quantify the effectiveness ... Voir le document complet
22
Modeling 1D distributed-memory dense kernels for an asynchronous multifrontal sparse solver
... 2 Modeling Left-Looking and Right-Looking Computations We consider a distributed-memory dense partial factorization relying on a dyna- mic asynchronous pipelined ...allow for efficient pivot searches ... Voir le document complet
15
Derivative Half Gaussian Kernels and Shock Filter
... used for all the methods are also detailed. As far as the evaluation process is concerned, the Structural Similarity Index Measure (SSIM) [21] is reported and plotted as a function of the number of ... Voir le document complet
13
Distributed learning with kernels in wireless sensor networks for physical phenomena modeling and tracking
... derived for regression and classification with support vector machines, they include classical techniques such as least-squares methods and extend them to nonlinear functional ...the Gaussian kernel κ (x i ... Voir le document complet
5
hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R
... (simply Gaussian) as a function of the prior parameterization on the Student-t degrees of freedom parameter, which they took to be ν ∼ Exp(θ = ...framework for studying sensitivity to this ...“essentially ... Voir le document complet
45
Signal norm testing in additive and independant standard Gaussian noise
... 0 for some nominal values of its parameters, more or less big fluctuations around these nominal values can occur — due to environmental conditions for instance — and induce deviations of kθk around 0, where ... Voir le document complet
38
Sujets connexes