Results 1 to 10 of about 196,185 (268)

Supervised Machine Learning with Control Variates for American Option Pricing

Foundations of Computing and Decision Sciences, 2018
In this paper, we make use of a Bayesian (supervised learning) approach in pricing American options via Monte Carlo simulations. We first present Gaussian process regression (Kriging) approach for American options pricing and compare its performance in ...
Mu Gang, Godina Teodor, Maffia Antonio, Sun Yong Chao   +3 more
doaj   +1 more source

Empirical Gaussian Processes

CoRR
Gaussian processes (GPs) are powerful and widely used probabilistic regression models, but their effectiveness in practice is often limited by the choice of kernel function. This kernel function is typically handcrafted from a small set of standard functions, a process that requires expert knowledge, results in limited adaptivity to data, and imposes ...
Jihao Andreas Lin, Sebastian Ament, Louis C. Tiao, David Eriksson, Maximilian Balandat, Eytan Bakshy   +5 more
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Distributed Gaussian Processes

, 2015
Copyright © 2015 by the author(s).To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or ...
Deisenroth, MP, Ng, JW
openaire   +5 more sources

Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning

Sensors, 2020
In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated.
Willem Raes, Nicolas Knudde, Jorik De Bruycker, Tom Dhaene, Nobby Stevens   +4 more
doaj   +1 more source

Gaussian processes for inferring parton distributions

Journal of High Energy Physics
The extraction of parton distribution functions (PDFs) from experimental or lattice QCD data is an ill-posed inverse problem, where regularization strongly impacts both systematic uncertainties and the reliability of the results.
Yamil Cahuana Medrano, Hervé Dutrieux, Joseph Karpie, Kostas Orginos, Savvas Zafeiropoulos, on behalf of the HadStruc Collaboration   +5 more
doaj   +1 more source

Numerical Solutions of Hamilton-Jacobi Inequalities by Constrained Gaussian Process Regression

SICE Journal of Control, Measurement, and System Integration, 2018
This paper proposes numerical solutions of Hamilton-Jacobi inequalities based on constrained Gaussian process regression. While Gaussian process regression is a tool to estimate an unknown function from its input and output data conventionally, the ...
Kenji Fujimoto, Hirofumi Beppu, Yuji Takaki   +2 more
doaj   +1 more source

Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach

Sensors, 2020
Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors.
Jonas Beuchert, Friedrich Solowjow, Sebastian Trimpe, Thomas Seel   +3 more
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Machine-Learning Methods for Computational Science and Engineering

Computation, 2020
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing.
Michael Frank, Dimitris Drikakis, Vassilis Charissis   +2 more
doaj   +1 more source

Multi-target tracking of birds in complex low-altitude airspace based on GM_PHD filter

The Journal of Engineering, 2019
GM_PHD (Gaussian mixture of probability hypothesis density) cannot completely track multiple targets, such as the flying birds in the complex low-altitude airspace near the airport, due to the lack of the steps of birth detection, track extraction and ...
Tao Hong, Wu Zhang, Weishi Chen, Xiaolong Chen, Xinru Fu   +4 more
doaj   +1 more source

Regression with Gaussian Processes [PDF]

, 1997
The Bayesian analysis of neural networks is difficult because the prior over functions has a complex form, leading to implementations that either make approximations or use Monte Carlo integration techniques. In this paper I investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis to be carried out ...
openaire   +1 more source