Stochastic Differential Equations: Models and Numerics
by Anders Szepessy, et al.
Publisher: KTH 2010
Number of pages: 202
The goal of this course is to give useful understanding for solving problems formulated by stochastic differential equations models in science, engineering and mathematical finance. Typically, these problems require numerical methods to obtain a solution and therefore the course focuses on basic understanding of stochastic and partial differential equations to construct reliable and efficient computational methods.
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by Oliver Knill - Overseas Press
This text covers material of a basic probability course, discrete stochastic processes including Martingale theory, continuous time stochastic processes like Brownian motion and stochastic differential equations, estimation theory, and more.
by K. Ito - Tata Institute of Fundamental Research
The book discusses the elementary parts of Stochastic Processes from the view point of Markov Processes. Topics: Markov Processes; Srong Markov Processes; Multi-dimensional Brownian Motion; Additive Processes; Stochastic Differential Equations; etc.
by S. Watanabe - Tata Institute of Fundamental Research
The author's main purpose in these lectures was to study solutions of stochastic differential equations as Wiener functionals and apply to them some infinite dimensional functional analysis. This idea was due to P. Malliavin.
by H. Kunita - Tata Institute Of Fundamental Research
The author presents basic properties of stochastic flows, specially of Brownian flows and their relations with local characteristics and with stochastic differential equations. Various limit theorems for stochastic flows are presented.