Results 41 to 50 of about 5,652,238 (291)

Gaussian Processes for Blazar Variability Studies

Galaxies, 2017
This article briefly introduces Gaussian processes as a new approach for modelling time series in the field of blazar physics. In the second part of the paper, recent results from an application of GP modelling to the multi-wavelength light curves of the
Vassilis Karamanavis
doaj   +1 more source

On the Degeneracy between fσ₈ Tension and Its Gaussian Process Forecasting

Universe, 2022
In this Article, we reconstruct the growth and evolution of the cosmic structure of the Universe using Markov chain Monte Carlo algorithms for Gaussian processes.
Mauricio Reyes, Celia Escamilla-Rivera
doaj   +1 more source

Convolutional Gaussian Processes

CoRR, 2017
To appear in Advances in Neural Information Processing Systems 30 (NIPS 2017)
van der Wilk, Mark, Rasmussen, Carl Edward, Hensman, James   +2 more
openaire   +4 more sources

Gaussian processes in ball bearing prognostics

Dyna, 2017
In this work, vibration analysis and Gaussian Processes techniques are used in useful life prognostics of ball bearings. The database is provided by The Prognostics Data Repository from NASA, and shows the failure evolution in ball bearings.
Juan Fernando López-López, Jhouben Janyk Cuesta Ramirez, Hector Fabio Quintero-Riaza   +2 more
doaj   +1 more source

Gaussian Process for Trajectories

, 2023
The Gaussian process is a powerful and flexible technique for interpolating spatiotemporal data, especially with its ability to capture complex trends and uncertainty from the input signal. This chapter describes Gaussian processes as an interpolation technique for geospatial trajectories.
Kien Nguyen 0003, John Krumm, Cyrus Shahabi   +2 more
openaire   +3 more sources

Gaussian process cosmography

Physical Review D, 2012
Gaussian processes provide a method for extracting cosmological information from observations without assuming a cosmological model. We carry out cosmography -- mapping the time evolution of the cosmic expansion -- in a model-independent manner using kinematic variables and a geometric probe of cosmology.
Shafieloo, Arman, Kim, Alex G., Linder, Eric V.   +2 more
openaire   +3 more sources

Modular Jump Gaussian Processes

Data Science in Science
Gaussian processes (GPs) furnish accurate nonlinear predictions with well-calibrated uncertainty. However, the typical GP setup has a built-in stationarity assumption, making it ill-suited for modeling data from processes with sudden changes, or “jumps ...
Anna R. Flowers, Christopher T. Franck, Mickaël Binois, Chiwoo Park, Robert B. Gramacy   +4 more
doaj   +1 more source

Integrated Gaussian Processes for Tracking

IEEE Open Journal of Signal Processing
In applications such as tracking and localisation, a dynamical model is typically specified for the modelling of an object's motion. An appealing alternative to the traditional parametric Markovian dynamical models is the Gaussian Process (GP ...
Fred Lydeard, Bashar I. Ahmad, Simon Godsill   +2 more
doaj   +1 more source

Additive Gaussian Processes

CoRR, 2011
Appearing in Neural Information Processing Systems ...
Duvenaud, D., Nickisch, H., Rasmussen, C.   +2 more
openaire   +6 more sources

Multiresolution Gaussian Processes

, 2012
We propose a multiresolution Gaussian process to capture long-range, non-Markovian dependencies while allowing for abrupt changes. The multiresolution GP hierarchically couples a collection of smooth GPs, each defined over an element of a random nested partition. Long-range dependencies are captured by the top-level GP while the partition points define
Emily B. Fox, David B. Dunson
openaire   +3 more sources