An introduction to stochastic differential equations
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
![](/opac-tmpl/bootstrap/itemtypeimg/npl/Rare-Book.gif)
Current library | Home library | Call number | Materials specified | Copy number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|
IMSc Library | IMSc Library | 519.216 EVA (Browse shelf (Opens below)) | Available | 77179 | |||
IMSc Library | IMSc Library | 519.216 EVA (Browse shelf (Opens below)) | 1 | Available | 77162 |
Browsing IMSc Library shelves Close shelf browser (Hides shelf browser)
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
||
517.98 KES Functional analysis | 519.21 PRA A First Course in Probability and Statistics | 519.216 EVA An introduction to stochastic differential equations | 519.216 EVA An introduction to stochastic differential equations | 519.216 STR Mathematics of probability | 519.216 STR Mathematics of probability | 519.23 FUR Ergodic theory and fractal geometry |
Introduction
A crash course in probability theory
Brownian motion and "white noise"
Stochastic integrals
Stochastic differential equations
Applications
This short book provides a quick, but very readable introduction to stochastic differential equations, that is, to differential equations subject to additive "white noise" and related random disturbances. The exposition is concise and strongly focused upon the interplay between probabilistic intuition and mathematical rigor. Topics include a quick survey of measure theoretic probability theory, followed by an introduction to Brownian motion and the Itô stochastic calculus, and finally the theory of stochastic differential equations. The text also includes applications to partial differential equations, optimal stopping problems and options pricing. This book can be used as a text for senior undergraduates or beginning graduate students in mathematics, applied mathematics, physics, financial mathematics, etc., who want to learn the basics of stochastic differential equations. The reader is assumed to be fairly familiar with measure theoretic mathematical analysis, but is not assumed to have any particular knowledge of probability theory (which is rapidly developed in Chapter 2 of the book).
There are no comments on this title.