Degree in MATHEMATICSEnglish
KNOWLEDGE AND UNDERSTANDING
At the end of the course the students will know some modern techniques in probability. Moreover, they will be able to use the studied methodologies in order to solve problems coming from the applications.
MAKING JUDGMENTS
At the end of the course the students will be able to recognize the fundamental characteristics of the considered problems and will have the
ability to appropriately choose the methods to solve them.
LEARNING SKILLS
At the end of the course the students will be able to consult texts of advanced probability
Basic probability and a good background in analysis
The purpose of the course is to present a comprehensive treatment of advanced probabilistic techniques for applications to stochastic modelling. In particular, Brownian motion, stochastic calculus and stochastic differential equations
Lectures
1 Stochastic processes
Definition and general facts. Kolmogorov’s continuity theorem. Construction of stochastic processes.
2 Brownian Motion
Definition and general facts. Wiener measure. Regularity of the path.
3 Martingales
Definitions and general facts. Discrete time martingales. Doob’s inequality. Continuous time martingales.
4 Markov Processes
Definitions and general facts. Semigroups and generators.
5 The Stochastic Integral
Introduction. Elementary processes.
6 Stochastic Calculus
Ito’s formula
7 Stochastic Differential Equations
Some examples
Oral exam
Any changes to the methods described here, which become necessary to
ensure the application of the safety protocols related to the COVID19
emergency, will be communicated on the websites of the course (moodle)
Baldi - Stochastic Calculus
An Introduction Through Theory and Exercises. Springer, 2017
