Marginalizing the Gaussian process and individual random
Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs
... on random walk kernels, the analogues of squared exponential kernels in Euclidean ...the random walk kernel should be normalised locally, so that each vertex has the same prior variance, and analyse the ...
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Multi-fidelity Gaussian process regression for prediction of random fields
... [25] ) to each component of the vector of Fourier coefficients. From a Bayesian standpoint, this is equivalent to assuming independent priors for each model output, which may result in loss of information. To overcome this ...
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Invariances of random fields paths, with applications in Gaussian Process Regression
... integrable random field through their covariance ...centred random fields with additive paths, and also to retrieve another result from [18] on kernels leading to random fields with paths invariant ...
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On degeneracy and invariances of random fields paths with applications in Gaussian process modelling
... of random fields with mo- tivating applications in Gaussian process ...of random field paths under some class of linear operators defined in terms of signed measures can be controlled through ...
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On degeneracy and invariances of random fields paths with applications in Gaussian process modelling
... Taking degeneracies and invariances into account in random field mod- elling is of practical interest, as illustrated in Section 4. Examples involving different kinds of structural priors show how GP prediction ...
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On Dynamical Gaussian Random Walks
... i.i.d. random variables, and to each ω j we associate a rate-one Poisson process with jump times 0 < τ j (1) < τ j (2) < ...Poisson process, we replace the existing ω-value by an independent ...
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Optimal Bayesian Estimation in Random Covariate Design with a Rescaled Gaussian Process Prior
... the Gaussian process prior assigns probability one to the smooth- ness class containing the true ...a Gaussian process with a squared-exponential kernel to enable better approximation of ...
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Marginalizing Prediction Functions
... A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin. Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal of Computational and Graphical ...
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A Gaussian Process Approach for Extended Object Tracking with Random Shapes and for Dealing with Intractable Likelihoods
... Department of Automatic Control and System Engineering, The University of Sheffield, Sheffield, UK S13JD Email:{ waftab1, ADeFreitas1, m.arvaneh, L.S.Mihaylova} @sheffield.ac.uk Abstract —Tracking of arbitrarily shaped ...
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On Fractional Gaussian Random Fields Simulations
... standard Gaussian variable instead of X(1) = ...The process at this new point is simulated as average of its two neighbors X(0) and X(1) plus a Gaussian variable with zero mean and appropriate ...the ...
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A comparative study of Gaussian geostatistical and Gaussian Markov random field models
... They showed that the matching correlation approach performed better than the KL method. From these earlier studies, it appears that one of the key elements of this comparative study is the choice of the metric to measure ...
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Definition of Probability Characteristics of the Absolute Maximum of Non-Gaussian Random Processes by Example of Hoyt Process
... non-Gaussian random processes is widely used in various areas of a science and technics ...stationary random function ...non-Gaussian random functions the Hoyt process [6] can be ...
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A comparative study of Gaussian geostatistical models and Gaussian Markov random field models
... The modeling of aggregated point-referenced data using GMRFs serves as one of our motivations to investigate the relations between GMRFs and GGMs. GGMs are used in modeling a process over a domain based upon a set ...
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Gaussian Fluctuations of Eigenvalues in Wigner Random Matrices
... a random point process with 2n + 1 particles. Then the new random point process formed by taking the n even particles has the same distribution as the eigenvalues of an n × n matrix from the ...
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Gaussian Process Dynamical Models
... Our work is motivated by modeling human motion for video-based people tracking and data-driven animation. Bayesian people tracking requires dynamical models in the form of transition densities in order to specify ...
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Gaussian Process Belief Propagation
... nonparametric random field models, each of which are well-understood and frequently used in Machine Learning and Statistics, should prove very rewarding in that models of complicated structure can be dealt with ...
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Gaussian process emulators for computer experiments with inequality constraints: Gaussian process emulators with inequality constraints
... a Gaussian process ...of Gaussian processes which converges uniformly ...and Gaussian random ...a Gaussian vector restricted to convex ...the Gaussian vector of ...
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Extremes of a(t)-locally stationary Gaussian random fields
... Stationary Gaussian Random Fields Enkelejd Hashorva and Lanpeng Ji 1 April 13, 2017 Abstract: The main result of this contribution is the derivation of the exact asymptotic behaviour of the supremum of a ...
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Critical and umbilical points of a non-Gaussian random field.
... physical process in which umbilical points can prove their usefulness is in the context of optical speckle ...a random pattern of intensity with approximately Gaussian ...sufficiently random. ...
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Packing dimension results for anisotropic Gaussian random fields
... present paper. For example, they may be useful for studying self-affine fractals. We should mention that another extended notion of packing dimension profiles has also been developed by Khoshnevisan, Schilling and Xiao ...
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