activity
20242026
collaborators

6 papers

stat.ME2026

A Taxonomy of Distance Metrics for Time-Sensitive Importance Splitting: Timer Bounds, Resampling, and the Global Age

Gabriel Dengler, Carlos E. Budde, Laura Carnevali

Importance splitting (ISPLIT) evaluates the probabilities of rare events in non-Markovian models. It requires a heuristic importance function (IFUN) that estimates the distance to…

cs.CR2026

How hard can it be? Quantifying MITRE attack campaigns with attack trees and cATM logic

Stefano M. Nicoletti, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga +2

The landscape of cyber threats grows more complex by the day. Advanced Persistent Threats carry out attack campaigns - e.g. operations Dream Job, Wocao, and WannaCry - against whic…

stat.ME2025

Statistical Model Checking Beyond Means: Quantiles, CVaR, and the DKW Inequality (extended version)

Carlos E. Budde, Arnd Hartmanns, Tobias Meggendorfer +2

Statistical model checking (SMC) randomly samples probabilistic models to approximate quantities of interest with statistical error guarantees. It is traditionally used to estimate…

cs.LO2025

Sound Statistical Model Checking for Probabilities and Expected Rewards (extended version)

Carlos E. Budde, Arnd Hartmanns, Tobias Meggendorfer +2

Statistical model checking estimates probabilities and expectations of interest in probabilistic system models by using random simulations. Its results come with statistical guaran…

cs.LO2025

Time-Sensitive Importance Splitting

Gabriel Dengler, Carlos E. Budde, Laura Carnevali +1

State-of-the-art methods for rare event simulation of non-Markovian models face practical or theoretical limits if observing the event of interest requires prior knowledge or infor…

cs.FL2024

Digging for Decision Trees: A Case Study in Strategy Sampling and Learning

Carlos E. Budde, Pedro R. D'Argenio, Arnd Hartmanns

We introduce a formal model of transportation in an open-pit mine for the purpose of optimising the mine's operations. The model is a network of Markov automata (MA); the optimisat…