Talks and Poster Presentations (with Proceedings-Entry):
A. Kofnov, M. Moosbrugger, S. Stankovic, E. Bartocci, E. Bura:
"Moment-based Invariants for Probabilistic Loops with non-polynomial assignments";
accepted as talk for: Proc. of QEST 2022: the 19th International Conference on Quantitative Evaluation of SysTems,
- 2022-09-16; in: "Proc. of QEST'22: the 19th International Conference on Quantitative Evaluation of SysTems",
We present a method to automatically approximate moment-based invariants of probabilistic programs with non-polynomial updates of continuous state variables to accommodate more complex dynamics. Our approach leverages polynomial chaos expansion to approximate non-linear functional updates as sums of orthogonal polynomials. We exploit this result to automatically estimate state-variable moments of all orders in Prob-solvable loops with non-polynomial updates. We showcase the accuracy of our estimation approach in several examples, such as the turning vehicle model and the Taylor rule in monetary policy.
Created from the Publication Database of the Vienna University of Technology.