Literatura académica sobre el tema "Regression Monte-Carlo scheme"
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Artículos de revistas sobre el tema "Regression Monte-Carlo scheme"
Izydorczyk, Lucas, Nadia Oudjane y Francesco Russo. "A fully backward representation of semilinear PDEs applied to the control of thermostatic loads in power systems". Monte Carlo Methods and Applications 27, n.º 4 (21 de octubre de 2021): 347–71. http://dx.doi.org/10.1515/mcma-2021-2095.
Texto completoFolashade Adeola Bolarinwa, Olusola Samuel Makinde y Olusoga Akin Fasoranbaku. "A new Bayesian ridge estimator for logistic regression in the presence of multicollinearity". World Journal of Advanced Research and Reviews 20, n.º 3 (30 de diciembre de 2023): 458–65. http://dx.doi.org/10.30574/wjarr.2023.20.3.2415.
Texto completoGobet, E., J. G. López-Salas, P. Turkedjiev y C. Vázquez. "Stratified Regression Monte-Carlo Scheme for Semilinear PDEs and BSDEs with Large Scale Parallelization on GPUs". SIAM Journal on Scientific Computing 38, n.º 6 (enero de 2016): C652—C677. http://dx.doi.org/10.1137/16m106371x.
Texto completoTrinchero, Riccardo y Flavio Canavero. "Use of an Active Learning Strategy Based on Gaussian Process Regression for the Uncertainty Quantification of Electronic Devices". Engineering Proceedings 3, n.º 1 (30 de octubre de 2020): 3. http://dx.doi.org/10.3390/iec2020-06967.
Texto completoGobet, Emmanuel, José Germán López-Salas y Carlos Vázquez. "Quasi-Regression Monte-Carlo Scheme for Semi-Linear PDEs and BSDEs with Large Scale Parallelization on GPUs". Archives of Computational Methods in Engineering 27, n.º 3 (4 de abril de 2019): 889–921. http://dx.doi.org/10.1007/s11831-019-09335-x.
Texto completoKhan, Sajid Ali, Sayyad Khurshid, Shabnam Arshad y Owais Mushtaq. "Bias Estimation of Linear Regression Model with Autoregressive Scheme using Simulation Study". Journal of Mathematical Analysis and Modeling 2, n.º 1 (29 de marzo de 2021): 26–39. http://dx.doi.org/10.48185/jmam.v2i1.131.
Texto completoWang, Han, Lingwei Xu y Xianpeng Wang. "Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network". Sensors 19, n.º 21 (4 de noviembre de 2019): 4789. http://dx.doi.org/10.3390/s19214789.
Texto completoSeo, Jung-In, Young Eun Jeon y Suk-Bok Kang. "New Approach for a Weibull Distribution under the Progressive Type-II Censoring Scheme". Mathematics 8, n.º 10 (5 de octubre de 2020): 1713. http://dx.doi.org/10.3390/math8101713.
Texto completoMORALES, MARÍA, CARMELO RODRÍGUEZ y ANTONIO SALMERÓN. "SELECTIVE NAIVE BAYES FOR REGRESSION BASED ON MIXTURES OF TRUNCATED EXPONENTIALS". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, n.º 06 (diciembre de 2007): 697–716. http://dx.doi.org/10.1142/s0218488507004959.
Texto completoMa, Zhi-Sai, Li Liu, Si-Da Zhou y Lei Yu. "Output-Only Modal Parameter Recursive Estimation of Time-Varying Structures via a Kernel Ridge Regression FS-TARMA Approach". Shock and Vibration 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/8176593.
Texto completoTesis sobre el tema "Regression Monte-Carlo scheme"
Min, Ming. "Numerical Methods for European Option Pricing with BSDEs". Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1169.
Texto completoIzydorczyk, Lucas. "Probabilistic backward McKean numerical methods for PDEs and one application to energy management". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAE008.
Texto completoThis thesis concerns McKean Stochastic Differential Equations (SDEs) to representpossibly non-linear Partial Differential Equations (PDEs). Those depend not onlyon the time and position of a given particle, but also on its probability law. In particular, we treat the unusual case of Fokker-Planck type PDEs with prescribed final data. We discuss existence and uniqueness for those equations and provide a probabilistic representation in the form of McKean type equation, whose unique solution corresponds to the time-reversal dynamics of a diffusion process.We introduce the notion of fully backward representation of a semilinear PDE: thatconsists in fact in the coupling of a classical Backward SDE with an underlying processevolving backwardly in time. We also discuss an application to the representationof Hamilton-Jacobi-Bellman Equation (HJB) in stochastic control. Based on this, we propose a Monte-Carlo algorithm to solve some control problems which has advantages in terms of computational efficiency and memory whencompared to traditional forward-backward approaches. We apply this method in the context of demand side management problems occurring in power systems. Finally, we survey the use of generalized McKean SDEs to represent non-linear and non-conservative extensions of Fokker-Planck type PDEs
Libros sobre el tema "Regression Monte-Carlo scheme"
Sobczyk, Eugeniusz Jacek. Uciążliwość eksploatacji złóż węgla kamiennego wynikająca z warunków geologicznych i górniczych. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 2022. http://dx.doi.org/10.33223/onermin/0222.
Texto completoCapítulos de libros sobre el tema "Regression Monte-Carlo scheme"
Habyarimana, Ephrem y Sofia Michailidou. "Genomic Prediction and Selection in Support of Sorghum Value Chains". En Big Data in Bioeconomy, 207–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_16.
Texto completoYoshida, Ruriko, Hisayuki Hara y Patrick M. Saluke. "Sequential Importance Sampling for Logistic Regression Model". En Computational Models for Biomedical Reasoning and Problem Solving, 231–55. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7467-5.ch009.
Texto completoActas de conferencias sobre el tema "Regression Monte-Carlo scheme"
Pidaparthi, Bharath y Samy Missoum. "A Multi-Fidelity Approach for Reliability Assessment Based on the Probability of Model Inconsistency". En ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90115.
Texto completoChao, Manuel Arias, Darrel S. Lilley, Peter Mathé y Volker Schloßhauer. "Calibration and Uncertainty Quantification of Gas Turbine Performance Models". En ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42392.
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