PROBABILITY
Instructional goals
The goal of this course is to introduce the student to the formal way to deal with uncertainty. The course aims at being rigorous, and complete although the technicalities will be contained. The course will cover the basic chapters of probability theory, with an introduction to martingales and their properties.
Intended learning outcomes
Expected Learning Outcomes
Upon completion of the course, students should have developed
1. a solid knowledge of the main tools of basic probability
2. a general understanding of the most common mathematical tools models used in Statistics, finance, econometrics etc.
3. the skill to model simple problems and pursue their actual solution by selecting the suitable probabilistic model for problem-solving.
4. the ability to apply autonomously the learned techniques to a variety of contexts, so that it will be possible to pursue further studies or to undertake post-graduate professional training courses.
5. the ability to use the language of probability and to communicate procedures and results, by adapting the concepts used to the interlocutor in the specific case.
Course Contents
Probability spaces - Axioms and properties of probability
Conditional probability and independence
Random variables
Discrete and continuous Distribution functions
Expectation and Moments
Moment Generating function
Sums of independent random variables
Convergence of random variables
Weak convergence
Laws of large numbers
Central limit theorem
Conditional expectation
Introduction to Martingales
Reference Books
Jacod, J. & Protter, P. Probability Essentials
Springer 2004
Brémaud, P.
Probability Theory and Stochastic Processes
Springer 2020
Lecture Notes
Teaching Methods
The whole course will be interactive and will require the students' active participation. Students will be strongly encouraged to form groups and engage in discussions so as to find themselves the solutions to the problems.
Assessment Method
To guarantee a continuous assessment, a written midterm will be open to attending students. Homework will proposed during the course The midterm will count for 30% of the final grade. It will be then necessary to sit for a second written test, after the end of the course, aimed at assessing the second half of the course and it will therefore count for the remaining percentage of the grade (70%). In alternative, students may opt to sit for a total written exam. Non attending students will have solely the option of the final written exam (100%). Attendance requirement: at least 70% of classes. Grade refusal is not allowed. Withdrawal from assessment activities (including final exam) is allowed up until the submission of the written exam or the official communication of the oral exam result.
Thesis assignment criteria
An interview to verify understanding and motivation.
Week 1
Session 1
Probability spaces – sigma algebras
Session 2
Properties of probability
Session 3
Uniform probability spaces
Week 2
Session 1
Conditional probability
Session 2
Independence
Session 3
Random variables
Week 3
Session 1
Independence of random variables
Session 2
Discrete random variables
Session 3
Continuous random variables
Week 4
Session 1
Transformation of random variables
Session 2
Joint and marginal distributions
Session 3
Sum of random variables
Week 5
Session 1
Expectation and Moments
Session 2
Covariance and correlation coefficient
Session 3
Multivariate Gaussian Densities
Week 6
Session 1
Conditional densities and moments
Session 2
Moment generating function
Session 3
Types of convergence
Week 7
Session 1
Inequalities
Session 2
Weak law of large numbers
Session 3
Strong law of large numbers
Week 8
Session 1 Exercises
Session 2
The Central Limit theorem
Session 3
applications of CLT
Week 9
Session 1
Conditional expectation
Session 2
Properties of conditional expectation
session 3
Martingales
Week 10
Session 1
Submartingales and supermartingales
Session 2
Martingale inequalities
Session 3 Exercises
Week 11
Session 1
Stopping times
Sessione 2
Doob's optional sampling theorem
Session 3
Martingale applications.
Week 12
If there is time: fundamentals of Markov chains. General Review and Exercises