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Статті в журналах з теми "Regression Monte-Carlo scheme"
Izydorczyk, Lucas, Nadia Oudjane, and 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, no. 4 (October 21, 2021): 347–71. http://dx.doi.org/10.1515/mcma-2021-2095.
Повний текст джерелаFolashade Adeola Bolarinwa, Olusola Samuel Makinde, and Olusoga Akin Fasoranbaku. "A new Bayesian ridge estimator for logistic regression in the presence of multicollinearity." World Journal of Advanced Research and Reviews 20, no. 3 (December 30, 2023): 458–65. http://dx.doi.org/10.30574/wjarr.2023.20.3.2415.
Повний текст джерелаGobet, E., J. G. López-Salas, P. Turkedjiev, and C. Vázquez. "Stratified Regression Monte-Carlo Scheme for Semilinear PDEs and BSDEs with Large Scale Parallelization on GPUs." SIAM Journal on Scientific Computing 38, no. 6 (January 2016): C652—C677. http://dx.doi.org/10.1137/16m106371x.
Повний текст джерелаTrinchero, Riccardo, and Flavio Canavero. "Use of an Active Learning Strategy Based on Gaussian Process Regression for the Uncertainty Quantification of Electronic Devices." Engineering Proceedings 3, no. 1 (October 30, 2020): 3. http://dx.doi.org/10.3390/iec2020-06967.
Повний текст джерелаGobet, Emmanuel, José Germán López-Salas, and 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, no. 3 (April 4, 2019): 889–921. http://dx.doi.org/10.1007/s11831-019-09335-x.
Повний текст джерелаKhan, Sajid Ali, Sayyad Khurshid, Shabnam Arshad, and Owais Mushtaq. "Bias Estimation of Linear Regression Model with Autoregressive Scheme using Simulation Study." Journal of Mathematical Analysis and Modeling 2, no. 1 (March 29, 2021): 26–39. http://dx.doi.org/10.48185/jmam.v2i1.131.
Повний текст джерелаWang, Han, Lingwei Xu, and Xianpeng Wang. "Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network." Sensors 19, no. 21 (November 4, 2019): 4789. http://dx.doi.org/10.3390/s19214789.
Повний текст джерелаSeo, Jung-In, Young Eun Jeon, and Suk-Bok Kang. "New Approach for a Weibull Distribution under the Progressive Type-II Censoring Scheme." Mathematics 8, no. 10 (October 5, 2020): 1713. http://dx.doi.org/10.3390/math8101713.
Повний текст джерелаMORALES, MARÍA, CARMELO RODRÍGUEZ, and ANTONIO SALMERÓN. "SELECTIVE NAIVE BAYES FOR REGRESSION BASED ON MIXTURES OF TRUNCATED EXPONENTIALS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 06 (December 2007): 697–716. http://dx.doi.org/10.1142/s0218488507004959.
Повний текст джерелаMa, Zhi-Sai, Li Liu, Si-Da Zhou, and 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.
Повний текст джерелаДисертації з теми "Regression Monte-Carlo scheme"
Min, Ming. "Numerical Methods for European Option Pricing with BSDEs." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1169.
Повний текст джерелаIzydorczyk, 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.
Повний текст джерелаThis 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
Книги з теми "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.
Повний текст джерелаЧастини книг з теми "Regression Monte-Carlo scheme"
Habyarimana, Ephrem, and Sofia Michailidou. "Genomic Prediction and Selection in Support of Sorghum Value Chains." In Big Data in Bioeconomy, 207–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_16.
Повний текст джерелаYoshida, Ruriko, Hisayuki Hara, and Patrick M. Saluke. "Sequential Importance Sampling for Logistic Regression Model." In 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.
Повний текст джерелаТези доповідей конференцій з теми "Regression Monte-Carlo scheme"
Pidaparthi, Bharath, and Samy Missoum. "A Multi-Fidelity Approach for Reliability Assessment Based on the Probability of Model Inconsistency." In 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.
Повний текст джерелаChao, Manuel Arias, Darrel S. Lilley, Peter Mathé, and Volker Schloßhauer. "Calibration and Uncertainty Quantification of Gas Turbine Performance Models." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42392.
Повний текст джерела