PROBABILITY I

Course objectives

General objectives: to acquire basic knowledge in probability theory. Specific objectives: Knowledge and understanding: at the end of the course the student will have acquired the basic notions and results related to probability theory on finite and countable spaces, to the concept of random discrete vectors and to the concept of continuous random variable. Applying knowledge and understanding: at the end of the course the student will be able to solve simple problems in discrete probability, problems concerning discrete random vectors and random numbers represented by continuous random variables. The student will also be able to understand the meaning and implications of independence and conditioning (in the context of discrete models), to understand the meaning of some fundamental limit theorems, such as the law of large numbers. Critical and judgmental skills: the student will have the bases to analyze the analogies and the relationships between the topics of the course with topics of mathematical analysis and combinatorics (acquired in the “Analisi I” course and treated in the course of “Fondamenti di Analisi Reale”). Communication skills: ability to expose the contents of the course in the oral part of the test and in any theoretical questions present in the written test. Learning skills: the acquired knowledge will allow a study, individual or given in a course related to more specialized aspects of probability theory.

Channel 1
GUSTAVO POSTA Lecturers' profile

Program - Frequency - Exams

Course program
- Probability axioms - Discrete probability spaces - Combinatorics - Inclusion-exclusion principle - Independence - Binomial, multinomial and hypergeometric distributions - Conditional probability - Continuity - Random walks, gambler ruin problem. - Discrete random variables: expectation, variance and covariance - Independence of random variables - Bernoulli, binomial, geometric and negative binomial random variables - Sums of independent random variables - Poisson random variable and its binomial approximation - Joint, marginal and conditional densities - Chebyshev's inequality and the weak law of large numbers - Conditional expectation - Continuous random variables: density function, distribution function, expectation and variance - Uniform random variable. Skorohod representation - Exponential random variable - Gaussian random variables and De Moivre-Laplace thorem
Prerequisites
The course requires familiarity with topics in the "Algebra Lineare" (Linear Algebra) course and the “Calcolo” (Calculus) course (sets, equivalence classes, functions, integrals, derivatives). This knowledge is indispensable. There are no compulsory propaedeutic courses.
Books
Q. Berger, F. Caravenna, P. Dai Pra: Probabilità (Springer) S. Ross: Probabilità. (Apogeo) F. Spizzichino, G. Nappo: Introduzione al calcolo delle probabilità. (Lecture Notes) Suggested exercises will be available.
Teaching mode
Theory (60%), Exercises (40%)
Frequency
Attendance at lessons is recommended, though it is not compulsory.
Exam mode
The examination aims to evaluate learning through a written test (consisting in solving problems similar to the one solved during the lectures) and an oral test (consisting in the discussion of the most relevant topics illustrated during the course). The written test will last about 3 hours and can be substituted by 2 intermediate tests, the first of which will take place mid-course and the second immediately at the end of the course. To pass the exam an evaluation of not less than 18/30 is needed. The student has to prove to have acquired sufficient knowledge in the topics of the program and to be able to perform at least the simplest of the assigned exercises. To achieve a score of 30/30 “cum laude”, the student must instead demonstrate that he has acquired excellent knowledge of all the topics covered during the course and be able to link them in a logical and coherent manner.
Bibliography
A. N. Shiryaev: Probability. W. Feller: An Introduction to Probability Theory and its Applications.
Lesson mode
Theory (60%), Exercises (40%)
ALESSANDRA FAGGIONATO Lecturers' profile
Channel 2
LORENZO BERTINI MALGARINI Lecturers' profile
  • Lesson code1022430
  • Academic year2025/2026
  • Coursecorso|33592
  • CurriculumMatematica per le applicazioni
  • Year1st year
  • Semester2nd semester
  • SSDMAT/06
  • CFU9
  • Subject areaFormazione Matematica Modellistico-Computazionale