MATHEMATICAL AND STATISTICAL METHODS

The aim of the course is to provide a brief introduction to the statistical methodologies, the probability, the Monte Carlo method and the Markov chains. For this purpose, classical differential and integral calculation tools will be used, and, for applications, the electronic spreadsheet and MATLAB. The course is aimed at students enrolled in science degree courses (Computer Science, Mathematics, Physics, Engineering, etc.).

Learning Objectives

Knowledge and understanding: the aim of the course is to acquire base knowledge that allow the student to understand theoretical and phisical mechanisms which constite the base for a communication system; in detail, the student will acquire knowledge of main computer networks protocols.

Applying knowledge and understanding: student will acquire knowledge usefull to configure a computer network of middle size, choosing the right devices , cabling the the net, optimizing the resource available. For this reason, in a part of the course will be presented practical aspects of computer network configuration.

Making judjements: through real examples of errors derived by wrong configurations, the student will be able to discover solutions for problems that he can find during his work as a network administrator.

Communication skills: student will acquire base communication skills using technical language in the field of computer network and information systems.

Learning skills: the course provides, to the student, teoretical and pratical methodologies in order to deal with new problems that can rise during his work activity

Classroom lessons. Classroom exercise on spreadsheet.

Descriptive statistics. Numerical representations of statistical data. Graphic representations of frequency distributions. Trends, variability and shape. Linear and nonlinear regression for a set of data. Exercises with electronic spreadsheet.

Probability elements. Some probability definitions. Axiomatic definition of probability. Conditional probability. Bayes theorem. Discrete and continuous random variables. Central trend indices and variability.

Main distributions. Distribution of Bernoulli, Binomial, Poisson, Exponential, Weibull, Normal, Chi-square, Student. *Convergence theorems. Distribution Convergence, Large Number law, Central Limit Theorem.

Parameter estimates. Sampling and samples. Major sampling distributions. Timely estimators and estimates. Interval estimates: average confidence intervals and variance. Examples


Hypothesis Test. General characteristics of a hypothesis test. Parametric tests. Examples. Non-parametric tests. Test for the goodness of the adaptation. Kolmogorov-Smirnov's Test. Chi-square Test. Exercises with electronic spreadsheet.

Random numbers. Generators based on linear occurrences. Statistical tests for random numbers. Generating random numbers with assigned probability density: direct technique, rejection, combined.

The Monte Carlo method. Recall for Numerical Integration Methods. Monte Carlo Algorithm "Hit or Miss". Monte Carlo sampling algorithm. Monte-Carlo sample-mean algorithm. Variance reduction techniques: importance sampling, control variations, stratified sampling, antithetic variations. Direct Monte Carlo simulation for semiconductors.

Markov chains. Definitions and generalities. Calculation of joint laws. Classification of states. Invariant probabilities. Steady state. The Metropolis Algorithm. Basic about theory of queues

1. Professor's notes

2. V. Romano, Metodi matematici per i corsi di ingegneria, Città Studi, 2018

3. P. Baldi, Calcolo delle probabilità e statistica , Mc Graw-Hill, Milano, 1992

4. M. Boella, Probabilità e statistica per ingegneria e scienze, Pearson Italia, 2010

5. F. Pelleray, Elementi di Statistica per le applicazioni, CELID, Torino