Precise simulations and analysis of aggregated probabilistic models
This project shall investigate a rich research line, recently pursued by a few within the Department of CS, looking at the development of quantitative abstractions of Markovian models. Abstractions come in the form of lumped, aggregated models, which are beneficial in being easier to simulate or to analyse. Key to the novelty of this work, the proposed abstractions are quantitative in that precise error bounds with the original model can be established. As such, whatever can be shown over the abstract model, can be as well formally discussed over the original one.
This project, grounded on existing literature, will pursue (depending on the student's interests) extensions of this recent work, or its implementation as a software tool.
Courses: Computer-Aided Formal Verification, Probabilistic Model Checking, Machine Learning