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Bibliografie tematiche / McKean stochastic differential equation

Letteratura scientifica selezionata sul tema "McKean stochastic differential equation"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "McKean stochastic differential equation":

1

Wang, Weifeng, Lei Yan, Junhao Hu e Zhongkai Guo. "An Averaging Principle for Mckean–Vlasov-Type Caputo Fractional Stochastic Differential Equations". Journal of Mathematics 2021 (16 luglio 2021): 1–11. http://dx.doi.org/10.1155/2021/8742330.

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In this paper, we want to establish an averaging principle for Mckean–Vlasov-type Caputo fractional stochastic differential equations with Brownian motion. Compared with the classic averaging condition for stochastic differential equation, we propose a new averaging condition and obtain the averaging convergence results for Mckean–Vlasov-type Caputo fractional stochastic differential equations.

2

Qiao, Huijie, e 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, n. 01 (marzo 2021): 2150006. http://dx.doi.org/10.1142/s0219025721500065.

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In this paper, the path independent property of additive functionals of McKean–Vlasov stochastic differential equations with jumps is characterized by nonlinear partial integro-differential equations involving [Formula: see text]-derivatives with respect to probability measures introduced by Lions. Our result extends the recent work16 by Ren and Wang where their concerned McKean–Vlasov stochastic differential equations are driven by Brownian motions.

3

Ma, Li, Fangfang Sun e Xinfang Han. "Controlled Reflected McKean–Vlasov SDEs and Neumann Problem for Backward SPDEs". Mathematics 12, n. 7 (31 marzo 2024): 1050. http://dx.doi.org/10.3390/math12071050.

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This paper is concerned with the stochastic optimal control problem of a 1-dimensional McKean–Vlasov stochastic differential equation (SDE) with reflection, of which the drift coefficient and diffusion coefficient can be both dependent on the state of the solution process along with its law and control. One backward stochastic partial differential equation (BSPDE) with the Neumann boundary condition can represent the value function of this control problem. Existence and uniqueness of the solution to the above equation are obtained. Finally, the optimal feedback control can be constructed by the BSPDE.

4

Narita, Kiyomasa. "The Smoluchowski–Kramers approximation for the stochastic Liénard equation by mean-field". Advances in Applied Probability 23, n. 2 (giugno 1991): 303–16. http://dx.doi.org/10.2307/1427750.

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The oscillator of the Liénard type with mean-field containing a large parameter α < 0 is considered. The solution of the two-dimensional stochastic differential equation with mean-field of the McKean type is taken as the response of the oscillator. By a rigorous evaluation of the upper bound of the displacement process depending on the parameter α, a one-dimensional limit diffusion process as α → ∞is derived and identified. Then our result extends the Smoluchowski–Kramers approximation for the Langevin equation without mean-field to the McKean equation with mean-field.

5

Narita, Kiyomasa. "The Smoluchowski–Kramers approximation for the stochastic Liénard equation by mean-field". Advances in Applied Probability 23, n. 02 (giugno 1991): 303–16. http://dx.doi.org/10.1017/s000186780002351x.

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Abstract (sommario):

The oscillator of the Liénard type with mean-field containing a large parameter α < 0 is considered. The solution of the two-dimensional stochastic differential equation with mean-field of the McKean type is taken as the response of the oscillator. By a rigorous evaluation of the upper bound of the displacement process depending on the parameter α, a one-dimensional limit diffusion process as α → ∞is derived and identified. Then our result extends the Smoluchowski–Kramers approximation for the Langevin equation without mean-field to the McKean equation with mean-field.

6

Pham, Huyên, e Xiaoli Wei. "Bellman equation and viscosity solutions for mean-field stochastic control problem". ESAIM: Control, Optimisation and Calculus of Variations 24, n. 1 (gennaio 2018): 437–61. http://dx.doi.org/10.1051/cocv/2017019.

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We consider the stochastic optimal control problem of McKean−Vlasov stochastic differential equation where the coefficients may depend upon the joint law of the state and control. By using feedback controls, we reformulate the problem into a deterministic control problem with only the marginal distribution of the process as controlled state variable, and prove that dynamic programming principle holds in its general form. Then, by relying on the notion of differentiability with respect to probability measures recently introduced by [P.L. Lions, Cours au Collège de France: Théorie des jeux à champ moyens, audio conference 2006−2012], and a special Itô formula for flows of probability measures, we derive the (dynamic programming) Bellman equation for mean-field stochastic control problem, and prove a verification theorem in our McKean−Vlasov framework. We give explicit solutions to the Bellman equation for the linear quadratic mean-field control problem, with applications to the mean-variance portfolio selection and a systemic risk model. We also consider a notion of lifted viscosity solutions for the Bellman equation, and show the viscosity property and uniqueness of the value function to the McKean−Vlasov control problem. Finally, we consider the case of McKean−Vlasov control problem with open-loop controls and discuss the associated dynamic programming equation that we compare with the case of closed-loop controls.

7

Bahlali, Khaled, Mohamed Amine Mezerdi e Brahim Mezerdi. "Stability of McKean–Vlasov stochastic differential equations and applications". Stochastics and Dynamics 20, n. 01 (12 giugno 2019): 2050007. http://dx.doi.org/10.1142/s0219493720500070.

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Abstract (sommario):

We consider McKean–Vlasov stochastic differential equations (MVSDEs), 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. This type of SDEs was studied in statistical physics and represents the natural setting for stochastic mean-field games. We will first discuss questions of existence and uniqueness of solutions under an Osgood type condition improving the well-known Lipschitz case. Then, we derive various stability properties with respect to initial data, coefficients and driving processes, generalizing known results for classical SDEs. Finally, we establish a result on the approximation of the solution of a MVSDE associated to a relaxed control by the solutions of the same equation associated to strict controls. As a consequence, we show that the relaxed and strict control problems have the same value function. This last property improves known results proved for a special class of MVSDEs, where the dependence on the distribution was made via a linear functional.

8

Bao, Jianhai, Christoph Reisinger, Panpan Ren e 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, n. 2245 (gennaio 2021): 20200258. http://dx.doi.org/10.1098/rspa.2020.0258.

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In this paper, we derive fully implementable first-order time-stepping schemes for McKean–Vlasov stochastic differential equations, allowing for a drift term with super-linear growth in the state component. We propose Milstein schemes for a time-discretized interacting particle system associated with the McKean–Vlasov equation and prove strong convergence of order 1 and moment stability, taming the drift if only a one-sided Lipschitz condition holds. To derive our main results on strong convergence rates, we make use of calculus on the space of probability measures with finite second-order moments. In addition, numerical examples are presented which support our theoretical findings.

9

Narita, Kiyomasa. "Asymptotic behavior of velocity process in the Smoluchowski–Kramers approximation for stochastic differential equations". Advances in Applied Probability 23, n. 2 (giugno 1991): 317–26. http://dx.doi.org/10.2307/1427751.

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Here a response of a non-linear oscillator of the Liénard type with a large parameter α ≥ 0 is formulated as a solution of a two-dimensional stochastic differential equation with mean-field of the McKean type. This solution is governed by a special form of the Fokker–Planck equation such as the Smoluchowski–Kramers equation, which is an equation of motion for distribution functions in position and velocity space describing the Brownian motion of particles in an external field. By a change of time and displacement we find that the velocity process converges to a one-dimensional Ornstein–Uhlenbeck process as α →∞.

10

Narita, Kiyomasa. "Asymptotic behavior of velocity process in the Smoluchowski–Kramers approximation for stochastic differential equations". Advances in Applied Probability 23, n. 02 (giugno 1991): 317–26. http://dx.doi.org/10.1017/s0001867800023521.

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Abstract (sommario):

Here a response of a non-linear oscillator of the Liénard type with a large parameter α ≥ 0 is formulated as a solution of a two-dimensional stochastic differential equation with mean-field of the McKean type. This solution is governed by a special form of the Fokker–Planck equation such as the Smoluchowski–Kramers equation, which is an equation of motion for distribution functions in position and velocity space describing the Brownian motion of particles in an external field. By a change of time and displacement we find that the velocity process converges to a one-dimensional Ornstein–Uhlenbeck process as α →∞.

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Tesi sul tema "McKean stochastic differential equation":

1

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.

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In this thesis, we study time-inhomogeneous and McKean-Vlasov type stochastic differential equations (SDEs), along with related partial differential equations (PDEs). We are particularly interested in regularity estimates and their applications to numerical methods. In the first part of the thesis, we build on the work of Kusuoka \& Stroock to develop sharp estimates on the derivatives of solutions to time-inhomogeneous parabolic PDEs. The basis of these estimates is an integration by parts formula for derivatives of the solution under the UFG condition, which is weaker than the uniform Hoermander condition. This integration by parts formula is obtained using Malliavin Calculus. The formula allows us to extend the notion of classical solution to a framework where differentiability does not necessarily hold in all directions. As an application, we extend the error analysis for the cubature on Wiener space method to time-inhomogeneous stochastic differential equations. We then present two cubature on Wiener space algorithms for the numerical solution of McKean-Vlasov SDEs with smooth scalar interaction. The analysis involves the regularity estimates proved previously and takes place under a uniform strong Hoermander condition. Finally, we develop integration by parts formulas on Wiener space for solutions of SDEs with general McKean-Vlasov interaction and uniformly elliptic coefficients. These formulas hold both for derivatives with respect to a real variable and derivatives with respect to a measure in the sense of Lions. This allows us to develop estimates on the density of solutions of the McKean-Vlasov SDEs. We also prove the existence of a classical solution to a related PDE with irregular terminal condition.

2

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.

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Nous considérons les équations différentielles stochastiques (EDS) de Mc Kean-Vlasov, qui sont des EDS dont les coefficients de dérive et de diffusion dépendent non seulement de l'état du processus inconnu, mais également de sa loi de probabilité. Ces EDS, également appelées EDS à champ moyen, ont d'abord été étudiées en physique statistique et représentent en quelque sorte le comportement moyen d'un nombre infini de particules. Récemment, ce type d'équations a suscité un regain d'intérêt dans le contexte de la théorie des jeux à champ moyen. Cette théorie a été inventée par P.L. Lions et J.M. Lasry en 2006, pour résoudre le problème de l'existence d'un équilibre de Nash approximatif pour les jeux différentiels, avec un grand nombre de joueurs. Ces équations ont trouvé des applications dans divers domaines tels que la théorie des jeux, la finance mathématique, les réseaux de communication et la gestion des ressources pétrolières. Dans cette thèse, nous avons étudié les questions de stabilité par rapport aux données initiales, aux coefficients et aux processus directeurs des équations de McKean-Vlasov. Les propriétés génériques de ce type d'équations stochastiques, telles que l'existence et l'unicité, la stabilité par rapport aux paramètres, ont été examinées. En théorie du contrôle, notre attention s'est portée sur l'existence et l'approximation de contrôles relaxés pour les systèmes gouvernés par des EDS de Mc Kean-Vlasov
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

3

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.

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Cette thèse s'intéresse aux équations différentielles stochastiques de type McKean(EDS) et à leur utilisation pour représenter des équations aux dérivées partielles (EDP) non linéaires. Ces équations ne dépendent pas seulement du temps et de la position d'une certaine particule mais également de sa loi. En particulier nous traitons le cas inhabituel de la représentation d'EDP de type Fokker-Planck avec condition terminale fixée. Nous discutons existence et unicité pour ces EDP et de leur représentation sous la forme d'une EDS de type McKean, dont l'unique solutioncorrespond à la dynamique du retourné dans le temps d'un processus de diffusion.Nous introduisons la notion de représentation complètement non-linéaire d'une EDP semilinéaire. Celle-ci consiste dans le couplage d'une EDS rétrograde et d'un processus solution d'une EDS évoluant de manière rétrograde dans le temps. Nous discutons également une application à la représentation d'une équation d'Hamilton-Jacobi-Bellman (HJB) en contrôle stochastique. Sur cette base, nous proposonsun algorithme de Monte-Carlo pour résoudre des problèmes de contrôle. Celui ciest avantageux en termes d'efficience calculatoire et de mémoire, en comparaisonavec les approches traditionnelles progressive rétrograde. Nous appliquons cette méthode dans le contexte de la gestion de la demande dans les réseaux électriques. Pour finir, nous faisons le point sur l'utilisation d'EDS de type McKean généralisées pour représenter des EDP non-linéaires et non-conservatives plus générales que Fokker-Planck
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

4

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.

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Dans cette thèse, nous proposons une approche progressive (forward) pour la représentation probabiliste d'Equations aux Dérivées Partielles (EDP) nonlinéaires et nonconservatives, permettant ainsi de développer un algorithme particulaire afin d'en estimer numériquement les solutions. Les Equations Différentielles Stochastiques Nonlinéaires de type McKean (NLSDE) étudiées dans la littérature constituent une formulation microscopique d'un phénomène modélisé macroscopiquement par une EDP conservative. Une solution d'une telle NLSDE est la donnée d'un couple $(Y,u)$ où $Y$ est une solution d' équation différentielle stochastique (EDS) dont les coefficients dépendent de $u$ et de $t$ telle que $u(t,cdot)$ est la densité de $Y_t$. La principale contribution de cette thèse est de considérer des EDP nonconservatives, c'est-à- dire des EDP conservatives perturbées par un terme nonlinéaire de la forme $Lambda(u,nabla u)u$. Ceci implique qu'un couple $(Y,u)$ sera solution de la représentation probabiliste associée si $Y$ est un encore un processus stochastique et la relation entre $Y$ et la fonction $u$ sera alors plus complexe. Etant donnée la loi de $Y$, l'existence et l'unicité de $u$ sont démontrées par un argument de type point fixe via une formulation originale de type Feynmann-Kac
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

5

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.

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Dans cette thèse, nous proposons une approche progressive (forward) pour la représentation probabiliste d'Equations aux Dérivées Partielles (EDP) nonlinéaires et nonconservatives, permettant ainsi de développer un algorithme particulaire afin d'en estimer numériquement les solutions. Les Equations Différentielles Stochastiques Nonlinéaires de type McKean (NLSDE) étudiées dans la littérature constituent une formulation microscopique d'un phénomène modélisé macroscopiquement par une EDP conservative. Une solution d'une telle NLSDE est la donnée d'un couple (Y,u) où Y est une solution d' équation différentielle stochastique (EDS) dont les coefficients dépendent de u et de t telle que u(t,.) est la densité de Yt. La principale contribution de cette thèse est de considérer des EDP nonconservatives, c'est-à- dire des EDP conservatives perturbées par un terme nonlinéaire de la forme Lambda(u,nabla u)u. Ceci implique qu'un couple (Y,u) sera solution de la représentation probabiliste associée si Y est un encore un processus stochastique et la relation entre Y et la fonction u sera alors plus complexe. Etant donnée la loi de Y, l'existence et l'unicité de u sont démontrées par un argument de type point fixe via une formulation originale de type Feynmann-Kac
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

6

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.

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7

Al-Saadony, Muhannad. "Bayesian stochastic differential equation modelling with application to finance". Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1530.

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In this thesis, we consider some popular stochastic differential equation models used in finance, such as the Vasicek Interest Rate model, the Heston model and a new fractional Heston model. We discuss how to perform inference about unknown quantities associated with these models in the Bayesian framework. We describe sequential importance sampling, the particle filter and the auxiliary particle filter. We apply these inference methods to the Vasicek Interest Rate model and the standard stochastic volatility model, both to sample from the posterior distribution of the underlying processes and to update the posterior distribution of the parameters sequentially, as data arrive over time. We discuss the sensitivity of our results to prior assumptions. We then consider the use of Markov chain Monte Carlo (MCMC) methodology to sample from the posterior distribution of the underlying volatility process and of the unknown model parameters in the Heston model. The particle filter and the auxiliary particle filter are also employed to perform sequential inference. Next we extend the Heston model to the fractional Heston model, by replacing the Brownian motions that drive the underlying stochastic differential equations by fractional Brownian motions, so allowing a richer dependence structure across time. Again, we use a variety of methods to perform inference. We apply our methodology to simulated and real financial data with success. We then discuss how to make forecasts using both the Heston and the fractional Heston model. We make comparisons between the models and show that using our new fractional Heston model can lead to improve forecasts for real financial data.

8

Li, Shuang. "Study of Various Stochastic Differential Equation Models for Finance". Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/56545.

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The first part of the study focuses on European and American option pricing. We explore the jump diffusion models with stochastic volatility within the general equilibrium framework and use the minimal martingale measure as martingale measure. The second part of the thesis is on portfolio optimization. We formulate optimal asset allocation problem with multiple-periods under mean variance utility in the game theoretic framework, develop and solve a series of extended HJB equations for the problem.

9

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.

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Stochastic differential equation mixed effects models (SDEMEMs) are increasingly used in biomedical and pharmacokinetic/pharmacodynamic research. However, the complexity of these models means that previous research has focussed on approximate parameter estimation methods. This thesis develops three novel Bayesian parameter estimation methods for SDEMEMs. The new methods can produce parameter estimates that are more accurate and provide more reliable uncertainty quantification. The new methods are applied to both real and simulated data from a tumour xenography study on mice.

10

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.

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Libri sul tema "McKean stochastic differential equation":

1

Peszat, S. Stochastic partial differential equations with Lévy noise: An evolution equation approach. Cambridge: Cambridge University Press, 2007.

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2

Prato, Giuseppe Da. Introduction to stochastic analysis and Malliavin calculus. Pisa, Italy: Edizioni della Normale, 2007.

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3

Tadahisa, Funaki, e Woyczyński W. A. 1943-, a cura di. Nonlinear stochastic PDE's: Hydrodynamic limit and Burgers' turbulence. New York: Springer, 1996.

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4

Frank, T. D. Nonlinear Fokker-Planck equations: Fundamentals and applications. Berlin: Springer, 2004.

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5

Sowers, R. B. Short-time geometry of random heat kernels. Providence, R.I: American Mathematical Society, 1998.

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6

Sowers, R. B. Short-time geometry of random heat kernels. Providence, R.I: American Mathematical Society, 1998.

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7

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.

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8

Soize, Christian. The Fokker-Planck equation for stochastic dynamical systems and its explicit steady state solutions. Singapore: World Scientific, 1994.

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9

Lawler, Gregory F. Random walk and the heat equation. Providence, R.I: American Mathematical Society, 2010.

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10

Pascal, Auscher, Coulhon T e Grigoryan A, a cura di. 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.

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Capitoli di libri sul tema "McKean stochastic differential equation":

1

Izydorczyk, Lucas, Nadia Oudjane e 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.

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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.

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Miller, Enzo, e 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.

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Chiarella, Carl, Xue-Zhong He e 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.

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Hirsch, Francis, Christophe Profeta, Bernard Roynette e 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.

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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.

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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.

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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.

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9

Atangana, Abdon, e 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.

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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.

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Atti di convegni sul tema "McKean stochastic differential equation":

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Granita e 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.

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Asano, T., T. Wada, M. Ohta e 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.

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Qi, Hongsheng, Junshan Lin, Yuyan Ying e 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.

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Zhang, Xiao, Wei Wei, Lei Zhang e 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.

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Lian, Baosheng, e 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.

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Vegh, Viktor, Zhengyi Yang, Quang M. Tieng e 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.

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Yang, Li, Shang-Pin Sheng, Romesh Saigal, Mingyan Liu, Dawei Chen e 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.

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Seok, Jinwuk, e 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.

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Sharifi, J., e 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.

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"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.

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Rapporti di organizzazioni sul tema "McKean stochastic differential equation":

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Kallianpur, G., e I. Mitoma. A Langevin-Type Stochastic Differential Equation on a Space of Generalized Functionals. Fort Belvoir, VA: Defense Technical Information Center, agosto 1988. http://dx.doi.org/10.21236/ada199809.

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Snyder, Victor A., Dani Or, Amos Hadas e S. Assouline. Characterization of Post-Tillage Soil Fragmentation and Rejoining Affecting Soil Pore Space Evolution and Transport Properties. United States Department of Agriculture, aprile 2002. http://dx.doi.org/10.32747/2002.7580670.bard.

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Abstract (sommario):

Tillage modifies soil structure, altering conditions for plant growth and transport processes through the soil. However, the resulting loose structure is unstable and susceptible to collapse due to aggregate fragmentation during wetting and drying cycles, and coalescense of moist aggregates by internal capillary forces and external compactive stresses. Presently, limited understanding of these complex processes often leads to consideration of the soil plow layer as a static porous medium. With the purpose of filling some of this knowledge gap, the objectives of this Project were to: 1) Identify and quantify the major factors causing breakdown of primary soil fragments produced by tillage into smaller secondary fragments; 2) Identify and quantify the. physical processes involved in the coalescence of primary and secondary fragments and surfaces of weakness; 3) Measure temporal changes in pore-size distributions and hydraulic properties of reconstructed aggregate beds as a function of specified initial conditions and wetting/drying events; and 4) Construct a process-based model of post-tillage changes in soil structural and hydraulic properties of the plow layer and validate it against field experiments. A dynamic theory of capillary-driven plastic deformation of adjoining aggregates was developed, where instantaneous rate of change in geometry of aggregates and inter-aggregate pores was related to current geometry of the solid-gas-liquid system and measured soil rheological functions. The theory and supporting data showed that consolidation of aggregate beds is largely an event-driven process, restricted to a fairly narrow range of soil water contents where capillary suction is great enough to generate coalescence but where soil mechanical strength is still low enough to allow plastic deforn1ation of aggregates. The theory was also used to explain effects of transient external loading on compaction of aggregate beds. A stochastic forInalism was developed for modeling soil pore space evolution, based on the Fokker Planck equation (FPE). Analytical solutions for the FPE were developed, with parameters which can be measured empirically or related to the mechanistic aggregate deformation model. Pre-existing results from field experiments were used to illustrate how the FPE formalism can be applied to field data. Fragmentation of soil clods after tillage was observed to be an event-driven (as opposed to continuous) process that occurred only during wetting, and only as clods approached the saturation point. The major mechanism of fragmentation of large aggregates seemed to be differential soil swelling behind the wetting front. Aggregate "explosion" due to air entrapment seemed limited to small aggregates wetted simultaneously over their entire surface. Breakdown of large aggregates from 11 clay soils during successive wetting and drying cycles produced fragment size distributions which differed primarily by a scale factor l (essentially equivalent to the Van Bavel mean weight diameter), so that evolution of fragment size distributions could be modeled in terms of changes in l. For a given number of wetting and drying cycles, l decreased systematically with increasing plasticity index. When air-dry soil clods were slightly weakened by a single wetting event, and then allowed to "age" for six weeks at constant high water content, drop-shatter resistance in aged relative to non-aged clods was found to increase in proportion to plasticity index. This seemed consistent with the rheological model, which predicts faster plastic coalescence around small voids and sharp cracks (with resulting soil strengthening) in soils with low resistance to plastic yield and flow. A new theory of crack growth in "idealized" elastoplastic materials was formulated, with potential application to soil fracture phenomena. The theory was preliminarily (and successfully) tested using carbon steel, a ductile material which closely approximates ideal elastoplastic behavior, and for which the necessary fracture data existed in the literature.