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Probability Theory


Probability theory


Academic year 2024/2025

Course ID
Laura Lea Sacerdote (Lecturer)
Bruno Toaldo (Lecturer)
Elena Issoglio (Lecturer)
1st year
Teaching period
First semester
D.M. 270 TAF B - Distinctive
Course disciplinary sector (SSD)
MAT/06 - probability and statistics
Class Lectures
Type of examination
Written and oral
An undergrauate level class in Probability. Good knowledge of real analysis and measure theory. Good abilities in elementary probabilistic problem solving are also necessary for the success in this class.
Propedeutic for
Stochastic Processes, Statistics for Stochastic Processes and EDS-Stochastic Dfferential Equations use concepts and tools introduced in this course.

Sommario del corso


Course objectives

Topics taught  in this class are essential tools required to a statistician and a probabilist. They are fundamental  for any modern mathematician. Students re-think to subjects  of their undergraduate studies with a different level of abstraction.This new approach allows them to control some  advanced methods of probability theory, useful for applications as well as  for research.


Results of learning outcomes

Knowledge and Understanding

Students attain a detailed knowledge of the foundations of modern theory of probability and related topics in measure theory.

Applying Knowledge and Understanding

Students attain good ability in probabilistic problem solving becoming able to deal both with theoretical and applied problems related with conditional expectation, convergence features, characteristic functions and martingales.

Making Judgements and Learning Skills

Students become able to prove new results related with the studied theory and to deepen topics by looking at suitable scientific articles and alternative textbooks.

Communication Skills

Students become able to properly use English and probability to correctly present in written and oral forms their theoretical studies and the results of exercises and homeworks.



  • Overview of elementary probability
  • Construction of probability measures on R and random variables
  • Integrals with respect to probability measures
  • Independent random variables
  • Distributions on Rn
  • Sums of random variables
  • 0-1 Laws
  • Convergence of sequences of random variables
  • Weak convergence and characteristic functions
  • Laws of large numbers and central limit theorem
  • Conditional expectations
  • Discrete time martingales
  • Optional stopping
  • Doob decomposition
  • Martingale inequalities
  • Convergence properties of discrete time martingales
  • Introduction to continuous time martingales

Course delivery

The teaching course is composed of 72 hours of lectures (including 16 hours of exercises). Supporting material will be made available on Moodle in due time.

The lectures will be in presence with exceptions in accordance with university regulations.


Learning assessment methods

The final exam includes both a written and an oral test. The written test is valid until the following oral exam and requires the solution of two exercises and the proof of a theorem, proposition, property (selected from those discussed during lectures). It is mandatory to pass this test to be admitted to the oral test. Only for the winter session, the written test is reduced to one exercise on martingales and the proof of a theorem if the student has passed the homeworks given during the lessons. The use of textbooks and personal notes during the written test is not allowed. The oral examination includes a discussion on the written test as well as two questions. The list of the possible questions for the oral examination will be provided in advance. The final grade is determined in the following way: let x = MAX(grade of written exam, grade of oral exam), y = MIN(grade of written exam, grade of oral exam); the final grade is given by z = 0.7*x + 0.3*y and then rounding it to the closest integer.

Students must register on the exams web-page to be admitted to the written/oral exams (in time). Unregistered students will not be admitted to exams being impossible to register their final mark.


Support activities

The teaching course include exercises classes; extra exercises are suggested as homework.

Suggested readings and bibliography



  • Çınlar, E., "Probability and Stochastics", Springer, 2011.
  • Schilling, R., Measures, Integrals and Martingales, 2nd edition, Cambridge University Press, 2017.
  • Durrett, R., "Probability: Theory and Examples", Cambridge, 2010.

Further suggested books: 

  • Williams, D., "Probability with Martingales", Cambridge University Press, 2001;
  • Shiryaev, A.N., "Probability", Springer, 1996;
  • Billingsey, P., "Probability and Measure", Wiley-Interscience, 1995;
  • Feller, W. "Introduction to Probability Theory and Applications", 2 Volumes, Wiley, 2008;
  • Varadhan, S.R.S., "Probability Theory", AMS, 2001;
  • Jacod, J., Protter, P., "Probability Essentials", Springer, 2004.


Class scheduleV

  • Closed
    Enrollment opening date
    01/09/2022 at 00:00
    Enrollment closing date
    30/06/2023 at 00:00
    Last update: 05/06/2024 12:58
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