Moment-based Invariants for Probabilistic Loops with Non-polynomial Assignments
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2022
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917881574326272 |
|---|---|
| author | Kofnov, Andrey Moosbrugger, Marcel Stankovič, Miroslav Bartocci, Ezio Bura, Efstathia |
| author_facet | Kofnov, Andrey Moosbrugger, Marcel Stankovič, Miroslav Bartocci, Ezio Bura, Efstathia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_02577 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Moment-based Invariants for Probabilistic Loops with Non-polynomial Assignments Kofnov, Andrey Moosbrugger, Marcel Stankovič, Miroslav Bartocci, Ezio Bura, Efstathia Applications Symbolic Computation 62G05 (Primary) 62P30 (Secondary) G.3 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. |
| title | Moment-based Invariants for Probabilistic Loops with Non-polynomial Assignments |
| topic | Applications Symbolic Computation 62G05 (Primary) 62P30 (Secondary) G.3 |
| url | https://arxiv.org/abs/2205.02577 |