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.
Download or read it online for free here:
by Alan Bain
An informal introduction to Stochastic Calculus, and especially to the Ito integral and some of its applications. The text concentrates on the parts of the course which the author found hard, there is little or no comment on more standard matters.
by Matt Scott - University of Waterloo
This book is designed as an introduction to the ideas and methods used to formulate mathematical models of physical processes in terms of random functions. A senior undergraduate course offered to students with a suitably mathematical background.
by F.P. Kelly - John Wiley and Sons Ltd
The book on vector stochastic processes in equilibrium or stochastic networks, with wide range of applications. It covers the concept of reversibility, the output from a queue, and the epolymerization process quilibrium distribution.
by S.P. Meyn, R.L. Tweedie - Springer
The book on the theory of general state space Markov chains, and its application to time series analysis, operations research and systems and control theory. An advanced graduate text and a monograph treating the stability of Markov chains.