MATHEMATICAL AND STATISTICAL METHODS FOR APPLICATIONS 2
Academic Year 2023/2024  Teacher: Vittorio ROMANOExpected Learning Outcomes
The aim of the course is to give the main tools for statistical investigations along with the study of advanced subjects for tackling problems of interest in mathematical physics, economics, industry and in general for applications arising from applied sciences. In particular, the course furnishes the background for mathematical analysis in economics and therefore suitable for student in the program of finance.
Some aspects treated in the course are in any case relevant for those who want to teach Mathematics at the high schools.
In particular, the course aims to allow the student to acquire the following skills:
knowledge and understanding: knowledge of results and fundamental methods in advanced statistics, stochastic processes and Monte Carlo method. Skill of understanding problems and to extract the major features. Skill of reading, understanding and analyzing a subject in the related literature and present it in a clear and accurate way.
applying knowledge and understanding: skill of elaborating new example or solving novel theoretical exsercise, looking for the most appopriate methods and applying them in an appropriate way.
making judgements: To be able of devise proposals suited to correctly interprete complex problems in advanced statistics, stochastic processes and Monte Carlo simulation. To be able to formulate autonomously adequate judgements on the applicablity of simulation methods or statistical or stochastic models to theoretical or real situations.
communication skills: skills of presenting arguments, problems, ideas and solutions in mathematical terms with clarity and accuracy and with procedures suited for the audience, both in an oral and a written form. Skill of clearly motivating the choice of the strategy, method and contents, along with the employed computational tools.
learning skills: reading and analyzing a subject in the literature involving applied mathematics. To tackle in an autonomuous way the systematic study of arguments not previously treated. To acquire a degree of autonomy such that the student can be able to start with an autonomuos reserach activity.
Course Structure
Mainly frontal lectures. Moreover, the theoretical acquired competencies will be applied in a laboratory where study cases will be tackled in a MATLAB environment.
If restrictions will be introduced because the COVID pandemic, le lectures will be given in a mixed way or only online and some changes could be introduced to assure the accomplishiments foreseen for the course.
Learning assessment may also be carried out on line, should the conditions require it.
Required Prerequisites
Attendance of Lessons
Detailed Course Content

Maximum likelihood method. Normal correlation. Bayesian statistical inference. Maximum entropy method: information theory of Shannon, statistical inference of distributions, application to the classical and quantum statical mechanics (deduction of the distributions of MaxwellBoltzmann, FermiDirac and BoseEinstein). Stochastic processes: general features, processes of Poisson and counting, symmetric and nonsymmetric random walk and continuous limit, Brownian motion, applications in finance, in biomathematics and physics. . Stochastic differential equations and Ito's formula. Markov chains: classification of the states, absorption probability into a closed class, invariant probability. Theorem of Metropolis and simulated annealing for stochastic optimization. Monte Carlo method: theoretical foundations, generation of random numbers, examples of applications (BernoulliLaplace diffusion, urn of Ehrenfest, evaluation of options in finance, Ising model).
Textbook Information
Notes of the lecturer
V. Romano, Metodi Matematici per i Corsi di Ingegneria, CittàStudi
P. Baldi Calcolo delle probabilità e statistica, McGrawHill
R. Scozzafava Incertezza e probabilità, Zanichelli
A. Rotondi, P. Pedroni, A. Pievatolo Probabilità Statistica e Simulazione, Springer
L. C. Evans, An introduction to stochastic differential equations, AMS
D. C. Montgomery, G. C. Runger Applied statistics and probability for engineers, J. Wiley
Course Planning
Subjects  Text References  

1  Metodo della massima verosimiglianza. Correlazione normale. Inferenza statistica Bayesiana. Metodo della massima entropia. Processi stocastici. Equazioni differenziali stocastiche. Metodi Monte Carlo.  notes of the lecturer 
Learning Assessment
Learning Assessment Procedures
If necessary the exam will be online.