Results 51 to 60 of about 851,390 (284)

Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection

Entropy, 2019
In 2000, Kennedy and O’Hagan proposed a model for uncertainty quantification that combines data of several levels of sophistication, fidelity, quality, or accuracy, e.g., a coarse and a fine mesh in finite-element simulations.
Sascha Ranftl, Gian Marco Melito, Vahid Badeli, Alice Reinbacher-Köstinger, Katrin Ellermann, Wolfgang von der Linden   +5 more
doaj   +1 more source

Strong Approximation of Empirical Copula Processes by Gaussian Processes

, 2011
We provide the strong approximation of empirical copula processes by a Gaussian process. In addition we establish a strong approximation of the smoothed empirical copula processes and a law of iterated ...
Adler R. J., Bouzebda S., Chung K.-L., Csörgő M., Csörgő M., Cui S., DasGupta A., Deheuvels P., Deheuvels P., Deheuvels P., Fermanian J.-D., Joe H., McNeil A. J., Moore D. S., Nelsen R. B., Piterbarg V. I., Salim Bouzebda, Shorack G. R., Sklar A., Sklar A., Tarek Zari, Tsukahara H.   +21 more
core   +1 more source

Polarization‐resolved femtosecond Vis/IR spectroscopy tailored for resolving weak signals in biological samples using minimal sample volume

FEBS Open Bio, EarlyView.
Unique biological samples, such as site‐specific mutant proteins, are available only in limited quantities. Here, we present a polarization‐resolved transient infrared spectroscopy setup with referencing to improve signal‐to‐noise tailored towards tracing small signals. We provide an overview of characterizing the excitation conditions for polarization‐
Clark Zahn, Karsten Heyne
wiley   +1 more source

WLAN monopole antenna design by Siamese convolutional neural network and KNN exploiting Gaussian process [PDF]

MATEC Web of Conferences
In the process of antenna design, surrogate models can generally be used, but modeling requires a large number of samples. Although full wave electromagnetic simulation software can handle this task, obtaining a large number of samples is time-consuming,
Tian Yubo, Meng Fei
doaj   +1 more source

On the Construction of Some Fractional Stochastic Gompertz Models

Mathematics, 2020
The aim of this paper is the construction of stochastic versions for some fractional Gompertz curves. To do this, we first study a class of linear fractional-integral stochastic equations, proving existence and uniqueness of a Gaussian solution.
Giacomo Ascione, Enrica Pirozzi
doaj   +1 more source

Deep Convolutional Gaussian Processes [PDF]

, 2020
We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared to current Gaussian process ...
Heinonen Markus, Kaski Samuel, Blomqvist Kenneth   +2 more
openaire   +2 more sources

Single‐molecule DNA flow‐stretch assays for high‐throughput DNA–protein interaction studies

FEBS Open Bio, EarlyView.
We describe an optimised single‐molecule DNA flow‐stretch assay that visualises DNA–protein interactions in real time. Linear DNA fragments are tethered to a surface and stretched by buffer flow for fluorescence imaging. Using λ and φX174 DNA, this protocol enhances reproducibility and accessibility, providing a versatile approach for studying diverse ...
Ayush Kumar Ganguli, Mohammad Nour Alsamsam, Ugnė Bagdonaitė, Van Truc Vu, Chun‐Jen Huang, Polina Kuzhir, Mindaugas Zaremba, Aurimas Kopūstas, Marijonas Tutkus   +8 more
wiley   +1 more source

Distributed Gaussian Processes With Uncertain Inputs

IEEE Access
Gaussian Process regression is a powerful non-parametric approach that facilitates probabilistic uncertainty quantification in machine learning. Distributed Gaussian Process (DGP) methods offer scalable solutions by dividing data among multiple GP models
Peter L. Green
doaj   +1 more source

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

Sensors, 2019
In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model,
Adolfo Molada-Tebar, Gabriel Riutort-Mayol, Ángel Marqués-Mateu, José Luis Lerma   +3 more
doaj   +1 more source

Stationary systems of Gaussian processes

, 2009
We describe all countable particle systems on $\mathbb{R}$ which have the following three properties: independence, Gaussianity and stationarity. More precisely, we consider particles on the real line starting at the points of a Poisson point process ...
Kabluchko, Zakhar
core   +2 more sources