Stochastic Modelling and Simulation

Learning Goals

Students will learn principles and methods of mathematical and computational modelling of population processes and stochastic differential equations, which have applications in several disciplines. Students will be capable of building and simulating efficiently a model of a complex system, by capturing the key features to be modelled and by understanding what kind of experimental data and information is available.

Program in pills

Discrete stochastic modelling. Markov chains in discrete and continuous time, discrete event simulation, population models. Continuous stochastic modeling: Stochastic Differential Equations (SDEs), Numerical Algorithms for SDEs. Stochastic approximations, parameter estimation and system design. Examples from systems biology, epidemiology, statistical physics, performance of computer networks, ecology.

Area

Mathematical and statistical modelling

Curriculum Foundations
TAF Type

D

Curriculum Industry
TAF Type

D

Curriculum Health
TAF Type

C

Curriculum Economy
TAF Type

D

SSD

INF/01

ECTS

6

Semester

2

Lecturers

Alberto D'Onofrio