The course will take place in room B1.6 (via Branze, 48, Brescia).

Course material is available on e-learning . Please contact the lecturer for enrolling in the course and having access to the material.

Schedule

The course will take place in the following days. The lecturer will provide the exact schedule to the enrolled students.

4 July
5 July
6 July
11 July
12 July
13 July

Abstract

If Math is the language of science, Stochastic Math is the language of Advanced Modeling and Performance Evaluation, from Event Driven Simulation (in the very end, nothing else than a Monte Carlo solution of a Semi-Markov Chain), to the analysis of Digital Communications and Computer Science, to Hidden Markov Chains that are one of the key ingredients of Computational Biology and of many applications of computational methods applied to Health Care, as well as one of the tools available in the Machine Learning zoo. This course, while trying to be very "light" in advanced math use, wants to lay a very solid theoretical background to tackle any (well, almost any!) scientific problem than needs to deal with non-deterministic phenomena or simply under-determined systems where lack of knowledge appears as random behavior, from bugs in software to errors in transmission systems, noise in electronic devices, disease spreading, efficiency of medical treatment, and many more. Modeling and Performance Evaluation are key ingredients of any engineering project, as well as Business Plans and Industrial Innovation: revenues for industries must be properly forecasted in advance (to decide if the initial investment is worth) and forecasting requires a model. A Stochastic Model simply adds details and probabilistic interpretation of future events, granting a much better understanding (compared to deterministic models) of the system under analysis and proper interpretation of risks and their consequences, from money loss, to risks for the environment and the society at large.