Gaussian process emulators for high dimensional output
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 random ...
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High-Dimensional Gaussian Process Bandits
... low dimensional, not necessarily axis-aligned, subspace and then applies Bayesian optimization on this estimated ...with Gaussian Process Upper Confidence Bound sampling in a carefully calibrated ...
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Gaussian Process Emulators in coastal wave modelling
... The LUT approach uses a regular grid technique to select the representative design events. For the selection of the design events, the input data are discretised into a LUT matrix, where a user selects a number of values ...
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Sparse Gaussian Process Emulators for surrogate design modelling
... a high dimensional parameter space where most of the parameters actively and significantly effect the ...the output space for optimal ...design process for adding additional simulator runs to ...
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Gaussian process emulators for uncertainty analysis in groundwater flow
... In the field of underground radioactive waste disposal, complex computer models are used to describe the flow of groundwater through rocks. An important property in this context is transmissivity, the ability of the ...
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Adaptive Gaussian process emulators for efficient reliability analysis
... combines Gaussian process-based optimisation and subset ...simulation. Gaussian process emulators construct a statistical ap- proximation to the output of the original code, ...
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Diagnostics and Simulation-Based Methods for Validating Gaussian Process Emulators
... Chapter 1 Introduction 1.1 Computer models In recent years, computer experiments have increasingly been used as replace- ments for physical experiments which are considered impractical, impossible or too costly. In order ...
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Bayesian sensitivity analysis of a 1D vascular model with Gaussian process emulators
... using Gaussian process emulators, compared to a standard Monte Carlo ...a Gaussian process for sensitivity analysis was of the order O (d) , rather than O (d × 10 3 ) needed for Monte ...
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Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
... Additionally our non-linear algorithm can be further kernelised leading to ‘twin kernel PCA’ in which a mapping between feature spaces occurs. 1 Introduction Visualisation of high dimensional data can be ...
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Bayesian Classification of High Dimensional Data with Gaussian Process using Different Kernels
... and high dimensional data subject to some redundancy, this is the gap this study is try to ...with Gaussian prior gave room for the formulation of kernel based learning algorithm in a Bayesian ...
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Sensitivity and uncertainty analysis of two human atrial cardiac cell models using gaussian process emulators
... using Gaussian process emulators can be used for a systematic and quantitive analysis of biophysically detailed cardiac cell ...approach Gaussian processes emulate the main features of the ...
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Approximate inference in related multi-output Gaussian Process Regression
... for Gaussian Process Regres- sion The above equation 19 describes the formulation for marginal ...have high confidence at the prediction points get more weight when compared to experts with low ...
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Variational Dependent Multi-output Gaussian Process Dynamical Systems
... Similarly, Damianou et al. (2011, 2014) extended the GP-LVM by imposing a dynamical prior on the latent space to the variational GP dynamical system (VGPDS). The nonlinear mapping from the latent space to the observation ...
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Variance Based Sensitivity Analysis of IKrIKr in a Model of the Human Atrial Action Potential Using Gaussian Process Emulators
... Recent studies have begun to address this problem by examining the sen- sitivity of model outputs such as action potential duration (APD) to variable model parameters [2,16]. These studies have concentrated on maximum ...
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Hierarchical and Multiple Output Gaussian Process for the analysis of Colombian Cellular Networks
... model can not learn or define a proper tree by its own, it is very useful in applications when we know the interaction of the output variables previously like the cell-sector re- lationship, skeletal structures of ...
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Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables
... In sharp contrast, the combination of EIS and sparse matrix algebra we propose requires computing times that are O(n δ ) with δ 3, keeping it computationally feasible even for high dimensions (n = 5000 + ). ...
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Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data
... P i ⊥ ( x ) Figure 2: The subspaces Ei and E ⊥ i of the ith class. number of parameters to estimate. It is indeed possible to add constraints on the different parameters to obtain more regularized models. Fixing the ...
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Quantifying Nonlocal Informativeness in High-Dimensional, Loopy Gaussian Graphical Models
... in Gaussian graphical models con- taining both cycles and ...data process- ing (active learning) ...for Gaussian mutual informa- tion exist, standard linear algebraic techniques, with complexity ...
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Multi-objective Infill Criterion Driven Gaussian Process Assisted Particle Swarm Optimization of High-dimensional Expensive Problems
... 1 In this paper, we call optimization problems with up to 30, from 30 to 50, and more than 50 decision variables, respectively, low-dimensional, medium- dimensional, and high-dimensional ...
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High dimensional output surrogate models for uncertainty and sensitivity analyses
... of emulators. For high- dimensional output problems, emulators must themselves be computationally ...of high-dimensional- output battery models is developed using a ...
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