Results 181 to 190 of about 204,078 (311)
An algorithm to simulate nonstationary and non-Gaussian stochastic processes. [PDF]
J Infrastruct Preserv Resil, 2021 Hong HP, Cui XZ, Qiao D.
europepmc +1 more source
Mean-Reverting Stochastic Processes, Evaluation of Forward Prices and Interest Rates
We consider mean-reverting stochastic processes and build self-consistent models for forward price dynamics and some applications in power industries. These models are built using the ideas and equations of stochastic differential geometry in order to ...
Makhankov, V. G., Aguero-Granados, M. A.
core
In this paper we study the detailed distributional properties of integrated non-Gaussian OU (intOU) processes. Both exact results and approximate results are given. We emphasise the study of the tail behaviour of the intOU process.
Neil Shephard, Ole E. Barndorff-Nielsen
core
Thermal Radiation Emission From 3D Graphene Networks
Advanced Materials Interfaces, EarlyView.Three‐dimensional graphene networks are demonstrated as exceptional broadband thermal emitters, exhibiting high‐intensity, spectrally homogeneous emission and strong mechanical flexibility compared to conventional emitters like silicon carbide.
Maria Chiara Paolozzi +7 more
wiley +1 more source
Symmetry Classes of Classical Stochastic Processes. [PDF]
J Stat PhysSá L, Ribeiro P, Prosen T, Bernard D.
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This paper discusses Bayesian inference for stochastic volatility models based on continuous superpositions of Ornstein-Uhlenbeck processes. These processes represent an alternative to the previously considered discrete superpositions.
Griffin, Jim, Steel, Mark F.J.
core
Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models. [PDF]
Continuous-time stochastic volatility models are becoming increasingly popular in finance because of their flexibility in accommodating most stylized facts of financial time series.
Emanuele Taufer +2 more
core
Advanced Design for Weakly Coupled Resonators by Automatic Active Optimization
Advanced Materials Technologies, EarlyView.An Automatic Active Optimization (AAO) strategy integrates machine learning predictors and genetic algorithms in a closed‐loop workflow. By iteratively expanding its dataset with new discoveries, AAO overcomes the limits of conventional methods. This approach finds superior microstructural designs beyond the initial sample space. We demonstrate this on
Wei Yue +8 more
wiley +1 more source
Learning stochastic processes with intrinsic noise from cross-sectional biological data. [PDF]
Proc Natl Acad Sci U S AMaddu S, Chardès V, Shelley MJ.
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