Titlebook: Quantitative Evaluation of Systems; 18th International C Alessandro Abate,Andrea Marin Conference proceedings 2021 Springer Nature Switzerl

Protein

Bogdan Ghi?,Asser Tantawi Entlastung bei der Weitergabe von Informationen und Wis- sen realisierbar ist. Die Entwicklung eines Benutzermodells für das individuelle Wissensmanagement basiert auf den Ergebnissen einer empirischen Studie mit mehreren hundert Teilnehmern. Dabei wird nicht nur herausgearbeitet, welche Informationen über d978-3-8244-0587-9978-3-322-83437-9

裁決

平常

Conference proceedings 2021gust 2021..The 21 full papers and 2 short papers presented together with 2 keynote papers were carefully reviewed and selected from 47 submissions. The papers are organized in the following topics: probabilistic model checking; quantitative models and metamodels: analysis and validation; queueing sy

竊喜

A Modest Approach to Dynamic Heuristic Search in Probabilistic Model Checking comparison to state-of-the-art model checkers in a large case study rooted in the well-established .. This study demonstrates that . is especially attractive to use on very large benchmark instances which are not solvable by any other tool.

喃喃訴苦

resilience

Basal-Ganglia

0302-9743 nce, in August 2021..The 21 full papers and 2 short papers presented together with 2 keynote papers were carefully reviewed and selected from 47 submissions. The papers are organized in the following topics: probabilistic model checking; quantitative models and metamodels: analysis and validation; q

調(diào)整

Quantifying Software Reliability via Model-Countingprogram translation, the reduction of C-program into SAT instances via software-bounded model-checker (.), and precise or approximate model-counting providing a reliable assessment. We evaluate our prototype implementation on over 24 examples with different model-counters. We show the feasibility of our pipeline and benefits against competitors.

Oscillate

FLAT

Active and Sparse Methods in Smoothed Model Checkingsteer the model-checking to more informative areas of the parameter space and thus improve sample efficiency. We demonstrate that online extensions of sparse variational Gaussian process inference algorithms provide a scalable method for implementing active learning approaches for smoothed model checking.