04642 - Probability Calculus and Statistics

Academic Year 2023/2024

  • Docente: Elena Bandini
  • Credits: 6
  • SSD: MAT/06
  • Language: Italian
  • Moduli: Elena Bandini (Modulo 1) Elena Bandini (Modulo 2)
  • Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
  • Campus: Bologna
  • Corso: First cycle degree programme (L) in Computer Science (cod. 8009)

Learning outcomes

At the end of the course, the student knows basic concepts and methods of probability and mathematical statistics. The student can solve simple problems of probability and statistical inference.

Course contents

Mathematical model of a random experiment: sample space, events, axioms of probability and their consequences.

Conditional probability and independence: chain rule, total probability rule and Bayes' rule.

Combinatorics and discrete uniform probability spaces.

Random variables:

  • Distribution (or law) and cumulative distribution function.
  • Discrete and (absolutely) continuous random variables: discrete and continuous probability density functions.
  • Expected value and variance.
  • Relevant probability distributions: Bernoulli, binomial, Poisson, discrete and continuous uniform, exponential, normal (or Gaussian).

Random vectors:

  • Joint law, marginal laws, joint cumulative distribution function, independence of random variables, covariance.
  • Discrete random vectors: joint discrete and marginal discrete probability density functions.

Descriptive statistics: population and sample, types of data, frequencies, tabular and graphical representations; measures of central tendency, measures of variability.

Bivariate data: joint frequencies and two-way tables; scatter plot; covariance and linear correlation coefficient; method of least squares and linear regression.

Limit theorems:

  • Sequence of i.i.d. random variables.
  • Law of large numbers: Chebyshev's inequality, Monte Carlo method.
  • Central limit theorem.
Discrete-time Markov chains: transition matrix, directed graph representation, n-step transition probability, communication classes, invariant distribution.


Lectures notes and sheets of exercises prepared by the teacher (available on virtuale.unibo.it in Italian).

Supplementary textbook: Sheldon M. Ross, Introduction to Probability and Statistics for Engineers and Scientists, fifth edition, Academic Press.

Teaching methods

Lectures and exercises will be alternated in order to explain theoretical concepts through examples.

Attendance is not compulsory but is strongly recommended, as active participation helps to gain a deeper comprehension of the topics treated in the course.

Lectures are in Italian.

Assessment methods

The assessment is based on a written exam, which lasts approximately two hours, and consists in four exercises concerning the subjects treated in the course.

The kind of exercises of the exam will be similar to those proposed on the "sheets of exercises" (available on virtuale.unibo.it).

Teaching tools

Website of the course available on virtuale.unibo.it, where the student can find: lecture notes, sheets of exercises, past written exams, and other useful material for the course.

Office hours

See the website of Elena Bandini