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"A systematic, self-contained treatment of the theory of stochastic differential equations in infinite dimensional spaces. Included is a discussion of Schwartz spaces of distributions in relation to probability theory and infinite dimensional stochastic analysis, as well as the random variables and stochastic processes that take values in infinite dimensional spaces."

"The stochastic stability of differential equations has become a very popular subject of research in mathematics and engineering. In this updated volume readers will find important new results on the moment Lyapunov exponent, stability index and some other fields, and a significantly expanded bibliography.This volume provides a solid foundation for to start their research or to study the properties of concrete mechanical systems subjected to random perturbations."

"Selected papers submitted by participants of the international Conference “Stochastic Analysis and Applied Probability 2010” ( www.saap2010.org ) make up the basis of this volume. The SAAP 2010 was held in Tunisia, from 7-9 October, 2010, and was organized by the “Applied Mathematics & Mathematical Physics” research unit of the preparatory institute to the military academies of Sousse (Tunisia), chaired by Mounir Zili. The papers cover theoretical, numerical and applied aspects of stochastic processes and stochastic differential equations. The study of such topic is motivated in part by the need to model, understand, forecast and control the behavior of many natural phenomena that evolve in time in a random way. Such phenomena appear in the fields of finance, telecommunications, economics, biology, geology, demography, physics, chemistry, signal processing and modern control theory, to mention just a few. As this book emphasizes the importance of numerical and theoretical studies of the stochastic differential equations and stochastic processes."

"Model-model Persamaan Diferensial Stokastik (PDS) memiliki peranan yang sangat penting di berbagai bidang industri, misalnya ekonomi, keuangan, biologi, kimia, epidemiologi, juga mikroelektronik (Higham D. J., 2001). Metode numerik seringkali digunakan untuk mengaproksimasi solusi dari suatu model PDS, sehingga dibutuhkan suatu proses komputasi untuk memperoleh solusi dari suatu model PDS tersebut. Model-model PDS biasanya melibatkan data dalam jumlah besar ataupun proses komputasi yang banyak, sehingga berdampak pada waktu komputasi yang semakin lama. Untuk mempercepat waktu komputasi, maka diterapkan komputasi paralel. Komputasi paralel adalah salah satu teknik melakukan komputasi secara bersamaan dengan memanfaatkan beberapa komputer/prosesor pada suatu waktu tertentu.Dalam skripsi ini diberikan algoritma paralel untuk mengaproksimasi solusi dari suatu model PDS. Algoritma-algoritma ini diimplementasikan dalam program yang dijalankan pada mesin multicore dengan MATLAB dan Parallel Computing Toolbox (versi trial). Diberikan juga kinerja algoritma paralel yang diukur dengan speed up dan efisiensi paralel.Stochastic Differential Equations (SDEs) models play a prominent role in a range of application areas, including biology, chemistry, epidemiology, mechanics, microelectronics, economics, and finance (Higham D. J., 2001). Numerical method is usually used to get an approximate solution of SDEs models which often involve huge data or many computation steps, hence need more computation time. Parallel computing is an alternative that can reduce the computation time.

This skripsi discuss some parallel techniques to solve SDEs problems especially in finance models. The parallel techniques is designed to utilize several processors simultaneously. In this case the algorithms run on multicore machine with MATLAB and Parallel Computing Toolbox (trial version). Parallel perfomance of the algorithms are also given which compared the speed up and efficiency of several parallel techniques."

"Effective dynamics of stochastic partial differential equations focuses on stochastic partial differential equations with slow and fast time scales, or large and small spatial scales. The authors have developed basic techniques, such as averaging, slow manifolds, and homogenization, to extract effective dynamics from these stochastic partial differential equations.The authors’ experience both as researchers and teachers enable them to convert current research on extracting effective dynamics of stochastic partial differential equations into concise and comprehensive chapters. The book helps readers by providing an accessible introduction to probability tools in Hilbert space and basics of stochastic partial differential equations. Each chapter also includes exercises and problems to enhance comprehension."