Computer Technologies Engineering

Information Systems Engineering

PROBABILISTIC METHODS FOR ENGINEERING

Teachers: 
Credits: 
6
Site: 
PARMA
Year of erogation: 
2021/2022
Unit Coordinator: 
Disciplinary Sector: 
Telecommunications
Semester: 
Second semester
Year of study: 
1
Language of instruction: 

Italian

Learning outcomes of the course unit

Understanding and capability to communicate the foundation of probability theory. Capability to solve exercises on probability theory, to use the specific functions, to recognize and employ the random variable models studied during the course.

Prerequisites

Mathematical analysis

Course contents summary

Introduction to descriptive statistics and inferential statistics. Elements of probability theory. Discrete and continuous random variables. Central limit theorem. Parameter estimation. Confidence intervals.

Course contents

Data organization e description, mean, median, mode, histograms, variance and standard deviation.
Normal model and correlation.
Sample space and events, probability axioms, binomial coefficient, conditional probability, Bayes' formula, independent events.
(approx. 6 hours)

Continuous and discrete random variables, probability density and cumulative functions, joint, conditional and marginal distributions, expected value, covariance, moment generating function. Random variables functions and transformations.
Random variable models: Bernoulli, Poisson, hypergeometric, binomial, uniform, normal, exponential, gamma, chi-square, t, F.
(approx. 24 hours)

Sample mean, central limit theorem, sample variance.
Maximum likelihood estimators, confidence intervals, bayesian estimators.
(approx. 18 hours)

Recommended readings

Sheldon M. Ross
Introduction to probability and statistics for engineers and scientists
Elsevier, fifth edition, 2014.

A. Bononi, G. Ferrari
"Introduzione a Teoria della probabilità e aleatorie con applicazioni all'ingegneria e alle scienze"
Soc. Esculapio, Bologna, aprile 2008.

Teaching methods

Classroom lectures and exercises.
Homework exercises in autonomy.
Software use (Matlab) for problem resolution.

Assessment methods and criteria

Written exam with possible supplementary oral.

Other informations

Classes will be held according to University instructions as regards the pandemic situation.