Academic literature on the topic 'McKean stochastic differential equation'
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Journal articles on the topic "McKean stochastic differential equation":
Wang, Weifeng, Lei Yan, Junhao Hu, and Zhongkai Guo. "An Averaging Principle for Mckean–Vlasov-Type Caputo Fractional Stochastic Differential Equations." Journal of Mathematics 2021 (July 16, 2021): 1–11. http://dx.doi.org/10.1155/2021/8742330.
Qiao, Huijie, and Jiang-Lun Wu. "Path independence of the additive functionals for McKean–Vlasov stochastic differential equations with jumps." Infinite Dimensional Analysis, Quantum Probability and Related Topics 24, no. 01 (March 2021): 2150006. http://dx.doi.org/10.1142/s0219025721500065.
Ma, Li, Fangfang Sun, and Xinfang Han. "Controlled Reflected McKean–Vlasov SDEs and Neumann Problem for Backward SPDEs." Mathematics 12, no. 7 (March 31, 2024): 1050. http://dx.doi.org/10.3390/math12071050.
Narita, Kiyomasa. "The Smoluchowski–Kramers approximation for the stochastic Liénard equation by mean-field." Advances in Applied Probability 23, no. 2 (June 1991): 303–16. http://dx.doi.org/10.2307/1427750.
Narita, Kiyomasa. "The Smoluchowski–Kramers approximation for the stochastic Liénard equation by mean-field." Advances in Applied Probability 23, no. 02 (June 1991): 303–16. http://dx.doi.org/10.1017/s000186780002351x.
Pham, Huyên, and Xiaoli Wei. "Bellman equation and viscosity solutions for mean-field stochastic control problem." ESAIM: Control, Optimisation and Calculus of Variations 24, no. 1 (January 2018): 437–61. http://dx.doi.org/10.1051/cocv/2017019.
Bahlali, Khaled, Mohamed Amine Mezerdi, and Brahim Mezerdi. "Stability of McKean–Vlasov stochastic differential equations and applications." Stochastics and Dynamics 20, no. 01 (June 12, 2019): 2050007. http://dx.doi.org/10.1142/s0219493720500070.
Bao, Jianhai, Christoph Reisinger, Panpan Ren, and Wolfgang Stockinger. "First-order convergence of Milstein schemes for McKean–Vlasov equations and interacting particle systems." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 477, no. 2245 (January 2021): 20200258. http://dx.doi.org/10.1098/rspa.2020.0258.
Narita, Kiyomasa. "Asymptotic behavior of velocity process in the Smoluchowski–Kramers approximation for stochastic differential equations." Advances in Applied Probability 23, no. 2 (June 1991): 317–26. http://dx.doi.org/10.2307/1427751.
Narita, Kiyomasa. "Asymptotic behavior of velocity process in the Smoluchowski–Kramers approximation for stochastic differential equations." Advances in Applied Probability 23, no. 02 (June 1991): 317–26. http://dx.doi.org/10.1017/s0001867800023521.
Dissertations / Theses on the topic "McKean stochastic differential equation":
McMurray, Eamon Finnian Valentine. "Regularity of McKean-Vlasov stochastic differential equations and applications." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/28918.
Mezerdi, Mohamed Amine. "Equations différentielles stochastiques de type McKean-Vlasov et leur contrôle optimal." Electronic Thesis or Diss., Toulon, 2020. http://www.theses.fr/2020TOUL0014.
We consider Mc Kean-Vlasov stochastic differential equations (SDEs), which are SDEs where the drift and diffusion coefficients depend not only on the state of the unknown process but also on its probability distribution. These SDEs called also mean- field SDEs were first studied in statistical physics and represent in some sense the average behavior of an infinite number of particles. Recently there has been a renewed interest for this kind of equations in the context of mean-field game theory. Since the pioneering papers by P.L. Lions and J.M. Lasry, mean-field games and mean-field control theory has raised a lot of interest, motivated by applications to various fields such as game theory, mathematical finance, communications networks and management of oil resources. In this thesis, we studied questions of stability with respect to initial data, coefficients and driving processes of Mc Kean-Vlasov equations. Generic properties for this type of SDEs, such as existence and uniqueness, stability with respect to parameters, have been investigated. In control theory, our attention were focused on existence, approximation of relaxed controls for controlled Mc Kean-Vlasov SDEs
Izydorczyk, 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.
This 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
Le, cavil Anthony. "Représentation probabiliste de type progressif d'EDP nonlinéaires nonconservatives et algorithmes particulaires." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLY023.
This thesis performs forward probabilistic representations of nonlinear and nonconservative Partial Differential Equations (PDEs), which allowto numerically estimate the corresponding solutions via an interacting particle system algorithm, mixing Monte-Carlo methods and non-parametric density estimates.In the literature, McKean typeNonlinear Stochastic Differential Equations (NLSDEs) constitute the microscopic modelof a class of PDEs which are conservative. The solution of a NLSDEis generally a couple $(Y,u)$ where $Y$ is a stochastic process solving a stochastic differential equation whose coefficients depend on $u$ and at each time $t$, $u(t,cdot)$ is the law density of the random variable $Y_t$.The main idea of this thesis is to consider this time a non-conservative PDE which is the result of a conservative PDE perturbed by a term of the type $Lambda(u, nabla u) u$. In this case, the solution of the corresponding NLSDE is again a couple $(Y,u)$, where again $Y$ is a stochastic processbut where the link between the function $u$ and $Y$ is more complicated and once fixed the law of $Y$, $u$ is determined by a fixed pointargument via an innovating Feynmann-Kac type formula
Le, cavil Anthony. "Représentation probabiliste de type progressif d'EDP nonlinéaires nonconservatives et algorithmes particulaires." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLY023.
This thesis performs forward probabilistic representations of nonlinear and nonconservative Partial Differential Equations (PDEs), which allowto numerically estimate the corresponding solutions via an interacting particle system algorithm, mixing Monte-Carlo methods and non-parametric density estimates.In the literature, McKean typeNonlinear Stochastic Differential Equations (NLSDEs) constitute the microscopic modelof a class of PDEs which are conservative. The solution of a NLSDEis generally a couple (Y,u) where Y is a stochastic process solving a stochastic differential equation whose coefficients depend on u and at each time t, u(t,.) is the law density of the random variable Yt.The main idea of this thesis is to consider this time a non-conservative PDE which is the result of a conservative PDE perturbed by a term of the type Lambda(u, nabla u) u. In this case, the solution of the corresponding NLSDE is again a couple (Y,u), where again Y is a stochastic processbut where the link between the function u and Y is more complicated and once fixed the law of Y, u is determined by a fixed pointargument via an innovating Feynmann-Kac type formula
Treacy, Brian. "A stochastic differential equation derived from evolutionary game theory." Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-377554.
Al-Saadony, Muhannad. "Bayesian stochastic differential equation modelling with application to finance." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1530.
Li, Shuang. "Study of Various Stochastic Differential Equation Models for Finance." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/56545.
Botha, Imke. "Bayesian inference for stochastic differential equation mixed effects models." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/198039/1/Imke_Botha_Thesis.pdf.
Zararsiz, Zarife. "On an epidemic model given by a stochastic differential equation." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-5747.
Books on the topic "McKean stochastic differential equation":
Peszat, S. Stochastic partial differential equations with Lévy noise: An evolution equation approach. Cambridge: Cambridge University Press, 2007.
Prato, Giuseppe Da. Introduction to stochastic analysis and Malliavin calculus. Pisa, Italy: Edizioni della Normale, 2007.
Tadahisa, Funaki, and Woyczyński W. A. 1943-, eds. Nonlinear stochastic PDE's: Hydrodynamic limit and Burgers' turbulence. New York: Springer, 1996.
Frank, T. D. Nonlinear Fokker-Planck equations: Fundamentals and applications. Berlin: Springer, 2004.
Sowers, R. B. Short-time geometry of random heat kernels. Providence, R.I: American Mathematical Society, 1998.
Sowers, R. B. Short-time geometry of random heat kernels. Providence, R.I: American Mathematical Society, 1998.
Dalang, Robert C. H\older-Sobolev regularity of the solution to the stochastic wave equation in dimension three. Providence, R.I: American Mathematical Society, 2009.
Soize, Christian. The Fokker-Planck equation for stochastic dynamical systems and its explicit steady state solutions. Singapore: World Scientific, 1994.
Lawler, Gregory F. Random walk and the heat equation. Providence, R.I: American Mathematical Society, 2010.
Pascal, Auscher, Coulhon T, and Grigoryan A, eds. Heat kernels and analysis on manifolds, graphs, and metric spaces: Lecture notes from a quarter program on heat kernels, random walks, and analysis on manifolds and graphs, April 16-July 13, 2002, Emile Borel Centre of the Henri Poincaré Institute, Paris, France. Providence, R.I: American Mathematical Society, 2003.
Book chapters on the topic "McKean stochastic differential equation":
Izydorczyk, Lucas, Nadia Oudjane, and Francesco Russo. "McKean Feynman-Kac Probabilistic Representations of Non-linear Partial Differential Equations." In Geometry and Invariance in Stochastic Dynamics, 187–212. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87432-2_10.
Kusuoka, Shigeo. "Stochastic Differential Equation." In Monographs in Mathematical Economics, 135–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8864-8_6.
Miller, Enzo, and Huyên Pham. "Linear-Quadratic McKean-Vlasov Stochastic Differential Games." In Modeling, Stochastic Control, Optimization, and Applications, 451–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25498-8_19.
Chiarella, Carl, Xue-Zhong He, and Christina Sklibosios Nikitopoulos. "The Stochastic Differential Equation." In Dynamic Modeling and Econometrics in Economics and Finance, 55–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45906-5_4.
Hirsch, Francis, Christophe Profeta, Bernard Roynette, and Marc Yor. "The Stochastic Differential Equation Method." In Peacocks and Associated Martingales, with Explicit Constructions, 223–64. Milano: Springer Milan, 2011. http://dx.doi.org/10.1007/978-88-470-1908-9_6.
Soize, Christian. "Markov Process and Stochastic Differential Equation." In Uncertainty Quantification, 41–59. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_3.
Zobitz, John M. "Statistics of a Stochastic Differential Equation." In Exploring Modeling with Data and Differential Equations Using R, 327–42. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003286974-26.
Fukushima, Masatoshi. "Dirichlet Forms, Caccioppoli Sets and the Skorohod Equation." In Stochastic Differential and Difference Equations, 59–66. Boston, MA: Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-1980-4_6.
Atangana, Abdon, and Seda İgret Araz. "Numerical Scheme for a General Stochastic Equation with Classical and Fractional Derivatives." In Fractional Stochastic Differential Equations, 61–82. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0729-6_4.
Ohkubo, Jun. "Solving Partial Differential Equation via Stochastic Process." In Lecture Notes in Computer Science, 105–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13523-1_13.
Conference papers on the topic "McKean stochastic differential equation":
Granita and Arifah Bahar. "Stochastic differential equation model to Prendiville processes." In THE 22ND NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM22): Strengthening Research and Collaboration of Mathematical Sciences in Malaysia. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4932498.
Asano, T., T. Wada, M. Ohta, and N. Takigawa. "Langevin equation as a stochastic differential equation in nuclear physics." In TOURS SYMPOSIUM ON NUCLEAR PHYSICS VI. AIP, 2007. http://dx.doi.org/10.1063/1.2713551.
Qi, Hongsheng, Junshan Lin, Yuyan Ying, and Jiahao Zhang. "Stochastic two dimensional car following model by stochastic differential equation." In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022. http://dx.doi.org/10.1109/itsc55140.2022.9921829.
Zhang, Xiao, Wei Wei, Lei Zhang, and Chen Ding. "Neural Stochastic Differential Equation for Hyperspectral Image Classification." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9555052.
Lian, Baosheng, and Fen Yang. "Stochastic differential equation with Pth linear growth condition." In 2011 International Conference on Information Science and Technology (ICIST). IEEE, 2011. http://dx.doi.org/10.1109/icist.2011.5765105.
Vegh, Viktor, Zhengyi Yang, Quang M. Tieng, and David C. Reutens. "Multimodal image registration using stochastic differential equation optimization." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5653395.
Yang, Li, Shang-Pin Sheng, Romesh Saigal, Mingyan Liu, Dawei Chen, and Qiang Zhang. "A stochastic differential equation model for spectrum utilization." In 2011 International Symposium of Modeling and Optimization of Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE, 2011. http://dx.doi.org/10.1109/wiopt.2011.5930019.
Seok, Jinwuk, and Changsik Cho. "Stochastic Differential Equation of the Quantization based Optimization." In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2022. http://dx.doi.org/10.1109/ictc55196.2022.9952667.
Sharifi, J., and H. Momeni. "Optimal control equation for quantum stochastic differential equations." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5717172.
"IMAGE DECONVOLUTION USING A STOCHASTIC DIFFERENTIAL EQUATION APPROACH." In Bayesian Approach for Inverse Problems in Computer Vision. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002064701570164.
Reports on the topic "McKean stochastic differential equation":
Kallianpur, G., and I. Mitoma. A Langevin-Type Stochastic Differential Equation on a Space of Generalized Functionals. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada199809.
Snyder, Victor A., Dani Or, Amos Hadas, and S. Assouline. Characterization of Post-Tillage Soil Fragmentation and Rejoining Affecting Soil Pore Space Evolution and Transport Properties. United States Department of Agriculture, April 2002. http://dx.doi.org/10.32747/2002.7580670.bard.