Parameters Estimation for Gaussian Processes
Gaussian semiparametric estimation of multivariate fractionally integrated processes
... We also prove the consistency of our multivariate GSE. Two-step estimation is partly motivated by its computational ease. However, in view of today’s computa- tional power, a maximization of the objective function ...
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Gaussian Semiparametric Estimation of Multivariate Fractionally Integrated Processes
... Two-step estimation is partly motivated by its computational ease, because a two-step estimation is faster in general than a high dimensional direct ...memory parameters is not likely to cause any ...
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Gaussian Processes for Signal Strength-Based Location Estimation
... model parameters, and their technique does not estimate the uncertainty in the measurement prediction, which is crucial for adequate likelihood ...estimate Gaussian likelihoods on a ...
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Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes
... three estimation strategies presented ...ferent estimation strategies based on simulated ...selected processes mix a special time-varying volatility structure with a non-Gaussian ...likelihood ...
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Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes
... Our second argument is related to Figure 2 and Figure 3. The first one provides the news im- pact curve obtained for each model with the different sets of estimated parameters. This concept introduced in the ...
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WiFi Position Estimation in Industrial Environments Using Gaussian Processes
... the parameters of the proposed signal strength attenuation model are empirically determined, and this information is used to triangulate the user’s position around a university campus ...
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Characteristic function estimation of non-Gaussian Ornstein-Uhlenbeck processes.
... AR(1) processes are those for which the one-dimensional marginal law is self-decomposable and similarly for the OU process, ...implement estimation of the parameters appearing in ...
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Sequential Parameter Estimation of Time Varying Non Gaussian Autoregressive Processes
... the parameters of a time-varying AR process which is driven by a non-Gaussian ...model parameters is unknown. The estimation is carried out by par- ticle filters, which produce samples and ...
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Gaussian process regression for the estimation of stable univariate time-series processes
... Σ β (i, j) = e −α ρ (|i−j|) e −α λ (i+j) The hyperparameters α ρ > 0, α λ > 0 will be learned from data and will define the prior knowledge on the process parameters. The value of α ρ determines the level of ...
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Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes
... parameter estimation of Gaussian processes is analyzed in an asymptotic frame- ...the estimation is improved when the regular grid is strongly ...covariance parameters, is analyzed in ...
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Modelling Annotator Bias with Multi task Gaussian Processes: An Application to Machine Translation Quality Estimation
... Baselines: The baselines use the SVM regres- sion algorithm with radial basis function kernel and parameters γ, and C optimised through grid- search and 5-fold cross validation on the training set. This is ...
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Cross-Validation Estimations of Hyper-Parameters of Gaussian Processes with Inequality Constraints
... In many situations physical systems may be known to satisfy inequality constraints with respect to some or all input parameters. When building a surrogate model of this system (like in the framework of computer ...
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Cross-validation estimations of hyper-parameters of Gaussian processes with inequality constraints
... a Gaussian process emulator such that all conditional simulations satisfy the inequality constraints in the whole domain 6 ...the estimation of covariance hyper-parameters and cross validation ...
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Parametric estimation for Gaussian longrange dependent processes based on the log-periodogram
... Departamento de MatemaÂticas, Instituto Venezolano de Investigaciones Cientõ®cas, Apartado 21827, Caracas 1020-A, Venezuela. E-mail: [email protected] We establish the consistency and asymptotic normality of a ...
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Statistical estimation of nonstationary Gaussian processes with long-range dependence and intermittency
... In this paper, we shall consider the case where the spectral densityof nonstationary Gaussian processes with stationaryincrements is of a general and 4exible form.. The spectral densityf[r] ...
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Deep Gaussian Processes
... We have introduced a framework for efficient Bayesian training of hierarchical Gaussian process mappings. Our approach approximately marginalises out the latent space, thus allowing for automatic structure ...
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Detecting periodicities with Gaussian processes
... ‘Quantifying the Periodicity t’ introduces a new criterion for measuring the periodicity of the signal. Finally, the last section illustrates the proposed approach on a biological case study where we detect, amongst the ...
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CONVEX BODIES AND GAUSSIAN PROCESSES
... (Accepted October 11, 2009) ABSTRACT For several decades, the topics of the title have had a fruitful interaction. This survey will describe some of these connections, including the GB/GC classification of convex bodies, ...
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Sparse Online Gaussian Processes
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Kv w pr u r v¦ò %²äxêdédè/]úoûvü¸ü+,þ!. ![r] ...
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Functional quantization of Gaussian processes
... stochastic processes ðX t Þ tA½0;1 viewed as L 2 ð½0; 1; dtÞ-valued random ...For Gaussian vectors and the L 2 -error we present detailed results for stationary and optimal ...
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