Courses in Stochastics in Spring 2007



Stefan Geiss

Stokastiset differentiaaliyhtälöt

(Stochastic differential equations)
MATS351, 9 op (5ov), 50 h

Time and Place:

Monday  12-14 MaD 380
Tuesday 08-10 MaD 380

First lecture:
08/01/2007


Description:
Stochastic differential equations are a fundamental tool in mathematics and in applications. Given a Brownian motion W=(W_t)_{t\ge 0}, a stochastic differential equation reads as

dX_t = a(t,X_t) dt + b(t,X_t) dW_t.

The solution should be a stochastic process (X_t)_{t\ge 0}. But what
is the meaning of this equation? Or going one step back: what is a
Brownian motion? Or, if we know this: what are the solutions, are they
unique, what properties do they have? The course intends to answer
(at least some of) the questions.

Contents of the lecture:
-) Brownian motion
-) Stochastic integrals
-) Ito's formula
-) Stochastic differential equations

Literature:
[1] I. Karatzas and A. Shreve: Brownian motion and stochastic calculus
[2] D. Revuz and M. Yor: Continuous martingales and Brownian motion

Script: here


Exercises:
Problems 15/01/2007

Problems 22/01/2007
Problems 29/01/2007
Problems 05/02/2007
Problems 12/02/2007
Problems 19/02/2007
Problems 26/02/2007
Problems 05/03/2007
Problems 12/03/2007
Problems 19/03/2007
Problems 26/03/2007
Problems 02/04/2007


 
Christel Geiss

Probability Theory 1
MATA261, 3ov (5 op), 34 h

Time and Place:
Tuesday       12-14 MaD 381
Thursday      12-14 MaD 381

First lecture: 09/01/2007

Description:  Probability theory is used understand random phenomena by mathematical means. In the course we will introduce probability measures
and study their properties. Then we will deal with random variables, relate independence to product spaces, and consider the expected value of a random variable. Finally, we will realise what is special about the Gaussian distribution that it can be used to model, for example, measuring
errors.

Probability Theory 1 is the basic course of the Stochastic Line, its content is needed in all other stochastic courses.

Literature:
[1] A. Gut: Probability: a graduate course (Springer)

Script: here

Exercises:
Problems 18/01/2007

Problems 25/01/2007

Problems 01/02/2007

Problems 08/02/2007
Problems 15/02/2007
Problems 22/02/2007
Problems 01/03/2007
Problems 08/03/2007
Problems 15/03/2007

topics for the test