Gaussian process emulation for probabilistic global
The confluence of Gaussian process emulation and wavelets
... is Gaussian process regression or kriging (Cressie 1993), which was already discussed in Chapter ...a Gaussian process to model the underlying function (or random field), we are making an ...
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Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models
... then be used as a computationally cheaper alternative to predict the simulation output for a large number of drug blocks. In previous work [ 9 ] a simple emulator based on linear interpolation from a multi-dimensional ...
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A comparison of polynomial chaos and Gaussian process emulation for uncertainty quantification in computer experiments
... 2.4. Summary Uncertainty quantification in computer experiments is an important and rapidly expanding field, applying to all areas of science which use simulators to conduct experiments about a physical phenomenon of ...
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Probabilistic uncertainty analysis of an FRF of a structure using a Gaussian process emulator
... for probabilistic uncertainty analysis of a fre- quency response function (FRF) of a structure obtained via a finite element (FE) ...the probabilistic uncertainty analysis of FRFs are ...
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Emulation of Poincaré return maps with Gaussian Kriging models
... Gussian process fitted to the data plays an important role to improve the flexibility of the multi-output emulator and it would be desirable to achieve this improvement by relaxing its assumption of a set of these ...
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Hybrid Probabilistic Wind Power Forecasting Using Temporally Local Gaussian Process
... respectively, and the generated residuals for each horizon fore- casting are divided into 30 even intervals according to their range. The number of residuals in each interval could be statis- tically counted and a bar ...
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Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
... novel probabilistic interpretation of principal component analysis (PCA) that we term dual probabilistic PCA ...through Gaussian processes. We refer to this model as a Gaussian process ...
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Using ice cores and Gaussian process emulation to recover changes in the Greenland Ice Sheet during the last interglacial
... a Gaussian process emulator as a statistical surrogate of the full climate ...fast, probabilistic predictions of the simulator outputs at untried ...
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Auto-Switch Gaussian Process Regression-Based Probabilistic Soft Sensors for Industrial Multigrade Processes with Transitions
... Prediction uncertainty has rarely been integrated into traditional soft sensors for industrial processes. As for a whole multi-grade process of several steady-state grades and corresponding transitions, it is ...
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Mapping the uncertainty in global CCN using emulation
... that these values are a result of simulation rather than analyti- cally calculated they are defined by ˆ V ∗ (E) and ˆ E ∗ (V ) here). The values in Table 2 are calculated by simulating 1000 pos- sible functions from the ...
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Gaussian Process Dynamical Models
... j ln β j up to an additive constant. We minimize L with respect to X , α, ¯ and β ¯ numerically. Figure 2 shows a GPDM 3D latent space learned from a human motion capture data com- prising three walk cycles. Each pose ...
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Gaussian Process Belief Propagation
... the Gaussian model is ...the global model, namely to compute marginal posterior beliefs of unobserved variables given data, belief propagation (BP) techiques can be employed, which essentially pass messages ...
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Several Theorems About Probabilistic Limiting Expressions: The Gaussian free field, symmetric Pearcey process, and strong Szegő asymptotics
... 6. Strong Szeg˝ o asymptotics of the Riemann ⇣ function Abstract. Assuming the Riemann hypothesis, we prove the weak convergence of linear statistics of the zeros of L-functions to a Gaussian field, with ...
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Gaussian process regression for binned data
... performing Gaussian Process (GP) regression given such binned data. Gaussian Processes are a principled probabilistic method for performing ...
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Approximations for Binary Gaussian Process Classification
... We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian process models for probabilistic binary classification. The relationships between several ...
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Stability of Controllers for Gaussian Process Dynamics
... Theorem 5 provides an asymptotic stability result for closed-loop control systems with dynamics given as the mean of a GP and can, thus, can be applied to a broad class of dynamics systems. However, the procedure is ...
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Probabilistic Feasibility for Nonlinear Systems with Non-Gaussian Uncertainty using RRT
... and Gaussian uncertainty, two assumptions which are often not applicable to many real-life path planning ...to Gaussian process noise, state distributions can be approximated as Gaussian by ...
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Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.
... individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using ...and global gray matter volume across the brain in ...
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Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation
... Acknowledgements This research was supported by the EPSRC (EP/ P016774/1) network M2D (Models-to-Decisions): Decision making under uncertainty, and the EPSRC Impact Acceleration Account Grant (EP/R51163811). SG and MH ...
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Automatic Gait Optimization with Gaussian Process Regression
... the process ex- ist, most involve local function optimization pro- cedures that suffer from three key ...on Gaussian process regression that addresses all three ...a global search strategy ...
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