Stochastic differential equations

Optional course 4 credits.

Theory + Exercises: 4 credits

I. Overview

This course provides :

- Brownian motions.

- Stochastic calculus and Stochastic differential equations..

II. Prerequisites

Analysis 1, 2, 3, Probability and statistic, Differential equations.

III. Contents  

Chapter 1. Basic probabilty theory.

1.1 . Random variables, distribution functions, density.

1.2 Independence of Random variables.

1.3. Conditional expectation E(X |Y).

Chapter 2. Stochastic processes

2.1. Stochastic processes.

2.2. Wiener processes (Brownian motions).

2.3. Ito integral, Ito formular. 

Chapter 3. Stochastic differential equations.

3.1. Solutions of stochastic differential equations.

3.2. Properties of solutions of stochastic differential equations

3.3. Modelling with stochastic differential equations.

References

 1. E. Allen - Modeling with Ito stochastic differential equations - Springer 2007
 2. Bernt Oksendal - Stochastic differential equations  6ed., Springer, 2003 .
 3. Lawrence C. Evans - Introduction to stochastic differential equations -Berkeley lecture notes