29 citations · 48 across the 11 of their papers we have counts for
11 papers
Polar: An Algebraic Analyzer for (Probabilistic) Loops
Marcel Moosbrugger, Julian Müllner, Ezio Bartocci +1
We present the Polar framework for fully automating the analysis of classical and probabilistic loops using algebraic reasoning. The central theme in Polar comes with handling alge…
Strong Invariants Are Hard: On the Hardness of Strongest Polynomial Invariants for (Probabilistic) Programs
Julian Müllner, Marcel Moosbrugger, Laura Kovács
We show that computing the strongest polynomial invariant for single-path loops with polynomial assignments is at least as hard as the Skolem problem, a famous problem whose decida…
Exact and Approximate Moment Derivation for Probabilistic Loops With Non-Polynomial Assignments
Andrey Kofnov, Marcel Moosbrugger, Miroslav Stankovič +2
Many stochastic continuous-state dynamical systems can be modeled as probabilistic programs with nonlinear non-polynomial updates in non-nested loops. We present two methods, one a…
(Un)Solvable Loop Analysis
Daneshvar Amrollahi, Ezio Bartocci, George Kenison +3
Automatically generating invariants, key to computer-aided analysis of probabilistic and deterministic programs and compiler optimisation, is a challenging open problem. Whilst the…
Automated Sensitivity Analysis for Probabilistic Loops
Marcel Moosbrugger, Julian Müllner, Laura Kovács
We present an exact approach to analyze and quantify the sensitivity of higher moments of probabilistic loops with symbolic parameters, polynomial arithmetic and potentially uncoun…
Moment-based Invariants for Probabilistic Loops with Non-polynomial Assignments
Andrey Kofnov, Marcel Moosbrugger, Miroslav Stankovič +2
We present a method to automatically approximate moment-based invariants of probabilistic programs with non-polynomial updates of continuous state variables to accommodate more com…
Solving Invariant Generation for Unsolvable Loops
Daneshvar Amrollahi, Ezio Bartocci, George Kenison +3
Automatically generating invariants, key to computer-aided analysis of probabilistic and deterministic programs and compiler optimisation, is a challenging open problem. Whilst the…
Distribution Estimation for Probabilistic Loops
Ahmad Karimi, Marcel Moosbrugger, Miroslav Stankovič +3
We present an algorithmic approach to estimate the value distributions of random variables of probabilistic loops whose statistical moments are (partially) known. Based on these mo…
This Is the Moment for Probabilistic Loops
Marcel Moosbrugger, Miroslav Stankovič, Ezio Bartocci +1
We present a novel static analysis technique to derive higher moments for program variables for a large class of probabilistic loops with potentially uncountable state spaces. Our…
The Probabilistic Termination Tool Amber
Marcel Moosbrugger, Ezio Bartocci, Joost-Pieter Katoen +1
We describe the Amber tool for proving and refuting the termination of a class of probabilistic while-programs with polynomial arithmetic, in a fully automated manner. Amber combin…
Automated Termination Analysis of Polynomial Probabilistic Programs
Marcel Moosbrugger, Ezio Bartocci, Joost-Pieter Katoen +1
The termination behavior of probabilistic programs depends on the outcomes of random assignments. Almost sure termination (AST) is concerned with the question whether a program ter…