Top PDF 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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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, ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
38