Relevant bibliographies by topics / Linear Stochastic Differential Equations

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Linear Stochastic Differential Equations'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Linear Stochastic Differential Equations"

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Buckdahn, Rainer. "Linear skorohod stochastic differential equations." Probability Theory and Related Fields 90, no. 2 (June 1991): 223–40. http://dx.doi.org/10.1007/bf01192163.

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Yong, Jiongmin. "Linear Forward—Backward Stochastic Differential Equations." Applied Mathematics and Optimization 39, no. 1 (January 2, 1999): 93–119. http://dx.doi.org/10.1007/s002459900100.

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Sykora, Henrik T., Daniel Bachrathy, and Gabor Stepan. "Stochastic semi‐discretization for linear stochastic delay differential equations." International Journal for Numerical Methods in Engineering 119, no. 9 (April 30, 2019): 879–98. http://dx.doi.org/10.1002/nme.6076.

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Español, Pep. "Stochastic differential equations for non-linear hydrodynamics." Physica A: Statistical Mechanics and its Applications 248, no. 1-2 (January 1998): 77–96. http://dx.doi.org/10.1016/s0378-4371(97)00461-5.

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Nguyen, Tien Dung. "LINEAR MULTIFRACTIONAL STOCHASTIC VOLTERRA INTEGRO-DIFFERENTIAL EQUATIONS." Taiwanese Journal of Mathematics 17, no. 1 (January 2013): 333–50. http://dx.doi.org/10.11650/tjm.17.2013.1728.

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Arnold, Ludwig, and Peter Imkeller. "Rotation Numbers For Linear Stochastic Differential Equations." Annals of Probability 27, no. 1 (January 1999): 130–49. http://dx.doi.org/10.1214/aop/1022677256.

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Török, C. "Numerical solution of linear stochastic differential equations." Computers & Mathematics with Applications 27, no. 4 (February 1994): 1–10. http://dx.doi.org/10.1016/0898-1221(94)90050-7.

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Gy�ngy, I., and E. Pardoux. "On quasi-linear stochastic partial differential equations." Probability Theory and Related Fields 94, no. 4 (December 1993): 413–25. http://dx.doi.org/10.1007/bf01192556.

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Dabrowski, Jacek. "Parameter identification in linear stochastic differential equations." Statistics & Probability Letters 7, no. 5 (April 1989): 391–94. http://dx.doi.org/10.1016/0167-7152(89)90092-8.

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Buckdahn, R., and D. Nualart. "Linear stochastic differential equations and Wick products." Probability Theory and Related Fields 99, no. 4 (December 1994): 501–26. http://dx.doi.org/10.1007/bf01206230.

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Dissertations / Theses on the topic "Linear Stochastic Differential Equations"

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Stanciulescu, Vasile Nicolae. "Selected topics in Dirichlet problems for linear parabolic stochastic partial differential equations." Thesis, University of Leicester, 2010. http://hdl.handle.net/2381/8271.

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This thesis is devoted to the study of Dirichlet problems for some linear parabolic SPDEs. Our aim in it is twofold. First, we consider SPDEs with deterministic coefficients which are smooth up to some order of regularity. We establish some theoretical results in terms of existence, uniqueness and regularity of the classical solution to the considered problem. Then, we provide the probabilistic representations (the averaging-over-characteristic formulas of its solution. We, thereafter, construct numerical methods for it. The methods are based on the averaging-over-characteristic formula and the weak-sense numerical integration of ordinary stochastic differential equations in bounded domains. Their orders of convergence in the mean-square sense and in the sense of almost sure convergence are obtained. The Monte Carlo technique is used for practical realization of the methods. Results of some numerical experiments are presented. These results are in agreement with the theoretical findings. Second, we construct the solution of a class of one dimensional stochastic linear heat equations with drift in the first Wiener chaos, deterministic initial condition and which are driven by a space-time white noise and the white noise. This is done by giving explicitly its Wiener chaos decomposition. We also prove its uniqueness in the weak sense. Then we use the chaos expansion in order to show that the unique weak solution is an analytic functional with finite moments of all orders. The chaos decomposition is also utilized as a very useful tool for obtaining a continuity property of the solution.
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Ali, Zakaria Idriss. "Existence result for a class of stochastic quasilinear partial differential equations with non-standard growth." Diss., University of Pretoria, 2010. http://hdl.handle.net/2263/29519.

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In this dissertation, we investigate a very interesting class of quasi-linear stochastic partial differential equations. The main purpose of this article is to prove an existence result for such type of stochastic differential equations with non-standard growth conditions. The main difficulty in the present problem is that the existence cannot be easily retrieved from the well known results under Lipschitz type of growth conditions [42].
Dissertation (MSc)--University of Pretoria, 2010.
Mathematics and Applied Mathematics
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Köhnlein, Dieter. "Asymptotisches Verhalten von Lösungen stochastischer linearer Differenzengleichungen im Rd." Bonn : [s.n.], 1988. http://catalog.hathitrust.org/api/volumes/oclc/20267120.html.

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Blöthner, Florian [Verfasser]. "Non-Uniform Semi-Discretization of Linear Stochastic Partial Differential Equations in R / Florian Blöthner." München : Verlag Dr. Hut, 2019. http://d-nb.info/1181514207/34.

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Pefferly, Robert J. "Finite difference approximations of second order quasi-linear elliptic and hyperbolic stochastic partial differential equations." Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/11244.

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This thesis covers topics such as finite difference schemes, mean-square convergence, modelling, and numerical approximations of second order quasi-linear stochastic partial differential equations (SPDE) driven by white noise in less than three space dimensions. The motivation for discussing and expanding these topics lies in their implications in such physical phenomena as signal and information flow, gravitational and electromagnetic fields, large scale weather systems, and macro-computer networks. Chapter 2 delves into the hyperbolic SPDE in one space and one time dimension. This is an important equation to such fields as signal processing, communications, and information theory where singularities propagate throughout space as a function of time. Chapter 3 discusses some concepts and implications of elliptic SPDE's driven by additive noise. These systems are key for understanding steady state phenomena. Chapter 4 presents some numerical work regarding elliptic SPDE's driven by multiplicative and general noise. These SPDE's are open topics in the theoretical literature, hence numerical work provides significant insight into the nature of the process. Chapter 5 presents some numerical work regarding quasi-geostrophic geophysical fluid dynamics involving stochastic noise and demonstrates how these systems can be represented as a combination of elliptic and hyperbolic components.
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Liu, Xuan. "Some contribution to analysis and stochastic analysis." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:485474c0-2501-4ef0-a0bc-492e5c6c9d62.

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The dissertation consists of two parts. The first part (Chapter 1 to 4) is on some contributions to the development of a non-linear analysis on the quintessential fractal set Sierpinski gasket and its probabilistic interpretation. The second part (Chapter 5) is on the asymptotic tail decays for suprema of stochastic processes satisfying certain conditional increment controls. Chapters 1, 2 and 3 are devoted to the establishment of a theory of backward problems for non-linear stochastic differential equations on the gasket, and to derive a probabilistic representation to some parabolic type partial differential equations on the gasket. In Chapter 2, using the theory of Markov processes, we derive the existence and uniqueness of solutions to backward stochastic differential equations driven by Brownian motion on the Sierpinski gasket, for which the major technical difficulty is the exponential integrability of quadratic processes of martingale additive functionals. A Feynman-Kac type representation is obtained as an application. In Chapter 3, we study the stochastic optimal control problems for which the system uncertainties come from Brownian motion on the gasket, and derive a stochastic maximum principle. It turns out that the necessary condition for optimal control problems on the gasket consists of two equations, in contrast to the classical result on ℝd, where the necessary condition is given by a single equation. The materials in Chapter 2 are based on a joint work with Zhongmin Qian (referenced in Chapter 2). Chapter 4 is devoted to the analytic study of some parabolic PDEs on the gasket. Using a new type of Sobolev inequality which involves singular measures developed in Section 4.2, we establish the existence and uniqueness of solutions to these PDEs, and derive the space-time regularity for solutions. As an interesting application of the results in Chapter 4 and the probabilistic representation developed in Chapter 2, we further study Burgers equations on the gasket, to which the space-time regularity for solutions is deduced. The materials in Chapter 4 are based on a joint work with Zhongmin Qian (referenced in Chapter 4). In Chapter 5, we consider a class of continuous stochastic processes which satisfy the conditional increment control condition. Typical examples include continuous martingales, fractional Brownian motions, and diffusions governed by SDEs. For such processes, we establish a Doob type maximal inequality. Under additional assumptions on the tail decays of their marginal distributions, we derive an estimate for the tail decay of the suprema (Theorem 5.3.2), which states that the suprema decays in a manner similar to the margins of the processes. In Section 5.4, as an application of Theorem 5.3.2, we derive the existence of strong solutions to a class of SDEs. The materials in this chapter is based on the work [44] by the author (Section 5.2 and Section 5.3) and an ongoing joint project with Guangyu Xi (Section 5.4).
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Fromm, Alexander. "Theory and applications of decoupling fields for forward-backward stochastic differential equations." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2015. http://dx.doi.org/10.18452/17115.

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Diese Arbeit beschäftigt sich mit der Theorie der sogenannten stochastischen Vorwärts-Rückwärts-Differentialgleichungen (FBSDE), welche als ein stochastisches Anologon und in gewisser Weise als eine Verallgemeinerung von parabolischen quasi-linearen partiellen Differentialgleichungen betrachtet werden können. Die Dissertation besteht aus zwei Teilen: In dem ersten entwicklen wir die Theorie der sogenannten Entkopplungsfelder für allgemeine mehrdimensionale stark gekoppelte FBSDE. Diese Theorie besteht aus Existenz- sowie Eindeutigkeitsresultaten basierend auf dem Konzept des maximalen Intervalls. Es beinhaltet darüberhinaus Werkzeuge um Regularität von konkreten Problemen zu untersuchen. Insgesamt wird die Theorie für drei Klassen von Problemen entwickelt: In dem ersten Fall werden Lipschitz-Bedingungen an die Parameter des Problems vorausgesetzt, welche zugleich vom Zufall abhängen dürfen. Die Untersuchung der beiden anderen Klassen basiert auf dem ersten. In diesen werden die Parameter als deterministisch vorausgesetzt. Gleichwohl wird die Lipschitz-Stetigkeit durch zwei verschiedene Formen der lokalen Lipschitz-Stetigkeit abgeschwächt. In dem zweiten Teil werden diese abstrakten Resultate auf drei konkrete Probleme angewendet: In der ersten Anwendung wird gezeigt wie globale Lösbarkeit von FBSDE in dem sogenannten nicht-degenerierten Fall untersucht werden kann. In der zweiten Anwendung wird die Lösbarkeit eines gekoppelten Systems gezeigt, welches eine Lösung zu dem Skorokhod''schen Einbettungproblem liefert. Die Lösung wird für den Fall einer allgemeinen nicht-linearen Drift konstruiert. Die dritte Anwendung führt auf Lösbarkeit eines komplexen gekoppelten Vorwärt-Rückwärts-Systems, aus welchem optimale Strategien für das Problem der Nutzenmaximierung in unvollständingen Märkten konstruiert werden. Das System wird in einem verhältnismäßig allgmeinen Rahmen gelöst, d.h. für eine verhältnismäßig allgemeine Klasse von Nutzenfunktion auf den reellen Zahlen.
This thesis deals with the theory of so called forward-backward stochastic differential equations (FBSDE) which can be seen as a stochastic formulation and in some sense generalization of parabolic quasi-linear partial differential equations. The thesis consist of two parts: In the first we develop the theory of so called decoupling fields for general multidimensional fully coupled FBSDE in a Brownian setting. The theory consists of uniqueness and existence results for decoupling fields on the so called the maximal interval. It also provides tools to investigate well-posedness and regularity for particular problems. In total the theory is developed for three different classes of FBSDE: In the first Lipschitz continuity of the parameter functions is required, which at the same time are allowed to be random. The other two classes we investigate are based on the theory developed for the first one. In both of them all parameter functions have to be deterministic. However, two different types of local Lipschitz continuity replace the more restrictive Lipschitz continuity of the first class. In the second part we apply these techniques to three different problems: In the first application we demonstrate how well-posedness of FBSDE in the so called non-degenerate case can be investigated. As a second application we demonstrate the solvability of a system, which provides a solution to the so called Skorokhod embedding problem (SEP) via FBSDE. The solution to the SEP is provided for the case of general non-linear drift. The third application provides solutions to a complex FBSDE from which optimal trading strategies for a problem of utility maximization in incomplete markets are constructed. The FBSDE is solved in a relatively general setting, i.e. for a relatively general class of utility functions on the real line.
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Cheng, Gang. "Analyzing and Solving Non-Linear Stochastic Dynamic Models on Non-Periodic Discrete Time Domains." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1236.

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Stochastic dynamic programming is a recursive method for solving sequential or multistage decision problems. It helps economists and mathematicians construct and solve a huge variety of sequential decision making problems in stochastic cases. Research on stochastic dynamic programming is important and meaningful because stochastic dynamic programming reflects the behavior of the decision maker without risk aversion; i.e., decision making under uncertainty. In the solution process, it is extremely difficult to represent the existing or future state precisely since uncertainty is a state of having limited knowledge. Indeed, compared to the deterministic case, which is decision making under certainty, the stochastic case is more realistic and gives more accurate results because the majority of problems in reality inevitably have many unknown parameters. In addition, time scale calculus theory is applicable to any field in which a dynamic process can be described with discrete or continuous models. Many stochastic dynamic models are discrete or continuous, so the results of time scale calculus are directly applicable to them as well. The aim of this thesis is to introduce a general form of a stochastic dynamic sequence problem on complex discrete time domains and to find the optimal sequence which maximizes the sequence problem.
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Mtiraoui, Ahmed. "I. Etude des EDDSRs surlinéaires II. Contrôle des EDSPRs couplées." Thesis, Toulon, 2016. http://www.theses.fr/2016TOUL0010/document.

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Cette thèse aborde deux sujets de recherches, le premier est sur l’existence et l’unicité des solutions des Équations Différentielles Doublement Stochastiques Rétrogrades (EDDSRs) et les Équations aux Dérivées partielles Stochastiques (EDPSs) multidimensionnelles à croissance surlinéaire. Le deuxième établit l’existence d’un contrôle optimal strict pour un système controlé dirigé par des équations différentielles stochastiques progressives rétrogrades (EDSPRs) couplées dans deux cas de diffusions dégénérée et non dégénérée.• Existence et unicité des solutions des EDDSRs multidimensionnels :Nous considérons EDDSR avec un générateur de croissance surlinéaire et une donnée terminale de carré intégrable. Nous introduisons une nouvelle condition locale sur le générateur et nous montrons qu’elle assure l’existence, l’unicité et la stabilité des solutions. Même si notre intérêt porte sur le cas multidimensionnel, notre résultat est également nouveau en dimension un. Comme application, nous établissons l’existence et l’unicité des solutions des EDPS semi-linéaires.• Contrôle des EDSPR couplées :Nous étudions un problème de contrôle avec une fonctionnelle coût non linéaire dont le système contrôlé est dirigé par une EDSPR couplée. L’objective de ce travail est d’établir l’existence d’un contrôle optimal dans la classe des contrôle stricts, donc on montre que ce contrôle vérifie notre équation et qu’il minimise la fonctionnelle coût. La méthode consiste à approcher notre système par une suite de systèmes réguliers et on montre la convergence. En passant à la limite, sous des hypothèses de convexité, on obtient l’existence d’un contrôle optimal strict. on suit cette méthode théorique pour deux cas différents de diffusions dégénérée et non dégénérée
In this Phd thesis, we considers two parts. The first one establish the existence and the uniquness of the solutions of multidimensional backward doubly stochastic differential equations (BDSDEs in short) and the stochastic partial differential equations (SPDEs in short) in the superlinear growth generators. In the second part, we study the stochastic controls problems driven by a coupled Forward-Backward stochastic differentialequations (FBSDEs in short).• BDSDEs and SPDEs with a superlinear growth generators :We deal with multidimensional BDSDE with a superlinear growth generator and a square integrable terminal datum. We introduce new local conditions on the generator then we show that they ensure the existence and uniqueness as well as the stability of solutions. Our work go beyond the previous results on the subject. Although we are focused on multidimensional case, the uniqueness result we establish is new in one dimensional too. As application, we establish the existence and uniqueness of probabilistic solutions tosome semilinear SPDEs with superlinear growth generator. By probabilistic solution, we mean a solution which is representable throughout a BDSDEs.• Controlled coupled FBSDEs :We establish the existence of an optimal control for a system driven by a coupled FBDSE. The cost functional is defined as the initial value of the backward component of the solution. We construct a sequence of approximating controlled systems, for which we show the existence of a sequence of feedback optimal controls. By passing to the limit, we get the existence of a feedback optimal control. The convexity condition is used to ensure that the optimal control is strict. In this part, we study two cases of diffusions : degenerate and non-degenerate
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Campos, Fabio Antonio Araujo de 1984. "Métodos matemáticos para o problema de acústica linear estocástica." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306070.

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Orientador: Maria Cristina de Castro Cunha
Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
Made available in DSpace on 2018-08-26T19:33:01Z (GMT). No. of bitstreams: 1 Campos_FabioAntonioAraujode_D.pdf: 1374668 bytes, checksum: 6318414d486cf4810705b84e0d722e77 (MD5) Previous issue date: 2015
Resumo: Neste trabalho estudamos o sistema de equações diferenciais estocásticas obtido na linearização do modelo de propagação de ondas acústicas. Mais especificamente, analisamos métodos para solução do sistema de equações diferenciais usado na acústica linear, onde a matriz com dados aleatórios e um vetor de funções aleatórias que define as condições iniciais. Além do tradicional Método de Monte Carlo aplicamos o Método de Transformações de Variáveis Aleatórias e o Método de Galerkin Estocástico. Apresentamos resultados obtidos usando diferentes distribuições de probabilidades dos dados do problema. Também comparamos os métodos através da distribuição de probabilidade e momentos estatísticos da solução
Abstract: On the present work we study the system of stochastic differential equations obtained from the linearization of the propagation model of acoustic waves. More specifically we analyze methods for the solution of the system of differential equations used in the linear acoustics, where the matrix with random data and a vector of random functions defining initial conditions. In addition to the traditional Monte Carlo Method we apply the Variable Transformations of Random Method and the Galerkin Stochastic Method. We present results obtained using different probability distributions of problem data. We also compared the methods through the distribution of probabilities and statistical moments of the solution
Doutorado
Matematica Aplicada
Doutor em Matemática Aplicada
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Books on the topic "Linear Stochastic Differential Equations"

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Stochastic evolution systems: Linear theory and applications to non-linear filtering. Dordrecht: Kluwer Academic Publishers, 1990.

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Gesztesy, Fritz, Harald Hanche-Olsen, Espen Jakobsen, Yurii Lyubarskii, Nils Henrik Risebro, and Kristian Seip, eds. Non-Linear Partial Differential Equations, Mathematical Physics, and Stochastic Analysis. Zuerich, Switzerland: European Mathematical Society Publishing House, 2018. http://dx.doi.org/10.4171/186.

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Rozovskii, B. L. Stochastic Evolution Systems: Linear Theory and Applications to Non-linear Filtering. Dordrecht: Springer Netherlands, 1990.

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Wu, Rangquan. Stochastic differential equations. Boston, Mass: Pitman Advanced, 1985.

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service), SpringerLink (Online, ed. Stochastic Differential Equations. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Stochastic differential equations. Boston: Pitman Advanced Pub. Program, 1985.

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Sobczyk, Kazimierz. Stochastic Differential Equations. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3712-6.

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Cecconi, Jaures, ed. Stochastic Differential Equations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11079-5.

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Øksendal, Bernt. Stochastic Differential Equations. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03620-4.

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Øksendal, Bernt. Stochastic Differential Equations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-14394-6.

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Book chapters on the topic "Linear Stochastic Differential Equations"

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Khasminskii, Rafail. "Systems of Linear Stochastic Equations." In Stochastic Stability of Differential Equations, 177–226. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23280-0_6.

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Da Prato, Giuseppe. "Non-linear Stochastic Partial Differential Equations." In Mathematics of Complexity and Dynamical Systems, 1126–36. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1806-1_68.

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Da Prato, Giuseppe. "Non-linear Stochastic Partial Differential Equations." In Encyclopedia of Complexity and Systems Science, 6228–39. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_367.

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Mohammed, S. E. A. "Almost surely non-linear solutions of stochastic linear delay equations." In Ordinary and Partial Differential Equations, 270–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0074735.

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Rozanov, Yuriĭ A. "Linear Stochastic Differential Equations and Linear Random Processes." In Introduction to Random Processes, 77–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-72717-7_12.

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Imkeller, Peter. "On the Laws of the Oseledets Spaces of Linear Stochastic Differential Equations." In Stochastic Differential and Difference Equations, 133–42. Boston, MA: Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-1980-4_11.

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Bhat, Harish S. "Algorithms for Linear Stochastic Delay Differential Equations." In Springer Proceedings in Mathematics & Statistics, 57–65. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2104-1_6.

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Küchler, Uwe, and Michael Sørensen. "Linear Stochastic Differential Equations with Time Delay." In Springer Series in Statistics, 135–56. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/0-387-22765-2_9.

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Shaikhet, Leonid. "Stability of Linear Scalar Equations." In Lyapunov Functionals and Stability of Stochastic Functional Differential Equations, 53–96. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00101-2_3.

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Privault, Nicolas. "Linear Skorohod stochastic differential equations on Poisson space." In Stochastic Analysis and Related Topics V, 237–53. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-2450-1_12.

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Conference papers on the topic "Linear Stochastic Differential Equations"

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GILSING, HAGEN. "ON ℒP-STABILITY OF NUMERICAL SCHEMES FOR LINEAR STOCHASTIC DELAY DIFFERENTIAL EQUATIONS." In Proceedings of the International Conference on Differential Equations. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812702067_0184.

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GOREAC, DAN. "APPROXIMATE CONTROLLABILITY FOR LINEAR STOCHASTIC DIFFERENTIAL EQUATIONS WITH CONTROL ACTING ON THE NOISE." In Applied Analysis and Differential Equations - The International Conference. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812708229_0013.

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Yu Zhiyong and Ji Shaolin. "Linear-quadratic nonzero-sum differential game of backward stochastic differential equations." In 2008 Chinese Control Conference (CCC). IEEE, 2008. http://dx.doi.org/10.1109/chicc.2008.4605519.

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HESSE, CHRISTIAN H. "A STOCHASTIC METHODOLOGY FOR NON-LINEAR PARTIAL DIFFERENTIAL EQUATIONS." In Proceedings of the Fourth International Conference. WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789814291071_0044.

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Mkhize, T. G., G. F. Oguis, K. Govinder, S. Moyo, and S. V. Meleshko. "Group classification of systems of two linear second-order stochastic ordinary differential equations." In MODERN TREATMENT OF SYMMETRIES, DIFFERENTIAL EQUATIONS AND APPLICATIONS (Symmetry 2019). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5125077.

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Bedziuk, Nadzeya V., and Aleh L. Yablonski. "Equations in differentials in the algebra of generalized stochastic processes." In Linear and Non-Linear Theory of Generalized Functions and its Applications. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2010. http://dx.doi.org/10.4064/bc88-0-3.

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Kolarova, Edita, and Lubomir Brancik. "Vector linear stochastic differential equations and their applications to electrical networks." In 2012 35th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2012. http://dx.doi.org/10.1109/tsp.2012.6256305.

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SEMOUSHIN, I. V. "IDENTIFYING PARAMETERS OF LINEAR STOCHASTIC DIFFERENTIAL EQUATIONS FROM INCOMPLETE NOISY MEASUREMENTS." In Proceedings of the International Conference on Inverse Problems. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704924_0026.

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Gangshi Hu, Yiming Lou, and Panagiotis D. Christofides. "Dynamic output feedback covariance control of linear stochastic dissipative partial differential equations." In 2008 American Control Conference (ACC '08). IEEE, 2008. http://dx.doi.org/10.1109/acc.2008.4586501.

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Hafstein, Sigurdur. "Lyapunov Functions for Linear Stochastic Differential Equations: BMI Formulation of the Conditions." In 16th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008192201470155.

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Reports on the topic "Linear Stochastic Differential Equations"

1

Christensen, S. K. Linear Stochastic Differential Equations on the Dual of a Countably Hilbert Nuclear Space with Applications to Neurophysiology. Fort Belvoir, VA: Defense Technical Information Center, June 1985. http://dx.doi.org/10.21236/ada159198.

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Christensen, S. K., and G. Kallianpur. Stochastic Differential Equations for Neuronal Behavior. Fort Belvoir, VA: Defense Technical Information Center, June 1985. http://dx.doi.org/10.21236/ada159099.

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Dalang, Robert C., and N. Frangos. Stochastic Hyperbolic and Parabolic Partial Differential Equations. Fort Belvoir, VA: Defense Technical Information Center, July 1994. http://dx.doi.org/10.21236/ada290372.

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Jiang, Bo, Roger Brockett, Weibo Gong, and Don Towsley. Stochastic Differential Equations for Power Law Behaviors. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada577839.

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Sharp, D. H., S. Habib, and M. B. Mineev. Numerical Methods for Stochastic Partial Differential Equations. Office of Scientific and Technical Information (OSTI), July 1999. http://dx.doi.org/10.2172/759177.

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Garrison, J. C. Stochastic differential equations and numerical simulation for pedestrians. Office of Scientific and Technical Information (OSTI), July 1993. http://dx.doi.org/10.2172/10184120.

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Jones, Richard H. Fitting Stochastic Partial Differential Equations to Spatial Data. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada279870.

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Xiu, Dongbin, and George E. Karniadakis. The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada460654.

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Budhiraja, Amarjit, Paul Dupuis, and Arnab Ganguly. Moderate Deviation Principles for Stochastic Differential Equations with Jumps. Fort Belvoir, VA: Defense Technical Information Center, January 2014. http://dx.doi.org/10.21236/ada616930.

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Chow, Pao-Liu, and Jose-Luis Menaldi. Stochastic Partial Differential Equations in Physical and Systems Sciences. Fort Belvoir, VA: Defense Technical Information Center, November 1986. http://dx.doi.org/10.21236/ada175400.

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