Uncertainty quantification with multiple response Gaussian processes 70
Gaussian processes with built-in dimensionality reduction: Applications in high-dimensional uncertainty quantification
... input uncertainty is described via a Gaussian random field, dimensionality reduction can be achieved via a truncated Karhunen-Lo`eve ex- pansion (KLE) ...
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Gaussian Process Modelling for Uncertainty Quantification in Convectively-Enhanced Dissolution Processes in Porous Media
... physico-chemical processes in deep aquifers are usually subject to uncertainty in one or more of the model input ...This uncertainty is propagated through the equations and needs to be quantified and ...
35
Gaussian process modelling for uncertainty quantification in convectively enhanced dissolution processes in porous media
... physico-chemical processes in deep aquifers are usually subject to uncertainty in one or more of the model input ...This uncertainty is propagated through the equations and needs to be quantified and ...
35
Deep Gaussian Processes and Variational Propagation of Uncertainty
... propagate uncertainty through a Gaussian process and obtain a rigorous lower bound on the marginal likelihood of the resulting ...Therefore, multiple scenarios can be accommodated; for example, if we ...
224
Adaptive multiple importance sampling for Gaussian processes
... the uncertainty in predictions or yield inaccurate assessment of the relative influence of different features ...a Gaussian likelihood and the more general case of non-Gaussian ...
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Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep
... the uncertainty in fingerprinting context as most would have ...the uncertainty of the signal ...the uncertainty from the collected fingerprints in Section ...and multiple realistically ...
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Computationally Efficient Convolved Multiple Output Gaussian Processes
... the response of the LMC and Figures 1(b) and 1(d) show the response of the convolved multiple output ...smoother response within the time ...convolved multiple output framework, allows ...
42
Methods for analysis and uncertainty quantification for processes recorded through sequences of images
... Figure 4.9: Example of spatial correction for two sets of r and γ. Initial labeling (left), corrected with r = 5, γ = 0.3 (center), and with r = 10, γ = 0.5 (right). The image was initially sampled at 10% coverage. 4.4 ...
225
Data-driven Demand Response Modeling and Control of Buildings with Gaussian Processes
... We adopt the MPC approach [13] to solve the demand tracking problem. At the core of the MPC are the models of the system, which are used to predict future system states given the current state and current and forecast ...
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Uncertainty quantification of the multi-centennial response of the Antarctic ice sheet to climate change
... AIS response to climate change based on numerical ice-sheet models remain chal- lenging due to the complexity of physical processes involved in ice-sheet dynamics, including instability mechanisms that can ...
32
Bayesian Methods For Uncertainty Quantification
... how it performs when discontinuities are present in the stochastic space. A very recent, theoretically sound way of modeling multiple outputs was developed in [19]. In this approach, the multidimensional ...
184
Discriminative training for Convolved Multiple-Output Gaussian processes
... Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class ...multi-output ...
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A comparison of polynomial chaos and Gaussian process emulation for uncertainty quantification in computer experiments
... 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 ...
210
Quantification of inverse response for controllability assessment of nonlinear processes
... project will deal with the design and synthesis of the controller. This work will specifically examine this technique of input-output linearization by feedback as a[r] ...
177
Hierarchical multiple output gaussian processes for human motion data
... the multiple output modelling, also was possible to use only two subjects and increase the amount of ICM to have an LMC for the multiple output modelling, being capable then of synthesizing more realistic ...
70
Quantification of Gaussian quantum steering
... bipartite Gaussian states of continuous variable systems. For two-mode Gaussian states, the measure reduces to a form of coherent information, which is proven never to exceed entanglement, and to reduce to ...
6
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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Error compensation and uncertainty evaluation of CMMs based on kinematic error models and gaussian processes
... and uncertainty to be calculated based upon the local, simplified kinematic error model, the propagation of uncertainty, and numerical ...experimental uncertainty determination according to the ISO ...
158
Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels
... model. In addition, we combined the multi-objective approach with multiple ker- nels to handle the challenges of selecting a particular kernel. For this, we used the weighted product of kernels where weights and ...
12
Uncertainty quantification for a model of HIV-1 patient response to antiretroviral therapy interruptions
... Our analysis of the correlation between estimated param- eters indicate that all of the differences in these parameters between patients reflect true distinctions in their immune system and HIV-1 replication kinetics. If ...
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