161,986 research outputs found
Linear distances between Markov chains
No full textWe introduce a general class of distances (metrics) between Markov chains, which are based on linear behaviour. This class encompasses distances given topologically (such as the total variation distance or trace distance) as well as by temporal logics or automata. We investigate which of the distances can be approximated by observing the systems, i.e. by black-box testing or simulation, and we provide both negative and positive results
Faster statistical model checking for unbounded temporal properties
No full textWe present a new algorithm for the statistical model checking of Markov chains with respect to unbounded temporal properties, including full linear temporal logic. The main idea is that we monitor each simulation run on the fly, in order to detect quickly if a bottom strongly connected component is entered with high probability, in which case the simulation run can be terminated early. As a result, our simulation runs are often much shorter than required by termination bounds that are computed a priori for a desired level of confidence on a large state space. In comparison to previous algorithms for statistical model checking our method is not only faster in many cases but also requires less information about the system, namely, only the minimum transition probability that occurs in the Markov chain. In addition, our method can be generalised to unbounded quantitative properties such as mean-payoff bounds
Faster statistical model checking for unbounded temporal properties
No full textWe present a new algorithm for the statistical model checking of Markov chains with respect to unbounded temporal properties, including full linear temporal logic. The main idea is that we monitor each simulation run on the fly, in order to detect quickly if a bottom strongly connected component is entered with high probability, in which case the simulation run can be terminated early. As a result, our simulation runs are often much shorter than required by termination bounds that are computed a priori for a desired level of confidence on a large state space. In comparison to previous algorithms for statistical model checking our method is not only faster in many cases but also requires less information about the system, namely, only the minimum transition probability that occurs in the Markov chain. In addition, our method can be generalised to unbounded quantitative properties such as mean-payoff bounds
[Report to Chief J. E. Curry, by an unknown author #1]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
[Report to Chief J. E. Curry, by an unknown author #2]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
Machine Learning and Model Checking Join Forces (Dagstuhl Seminar 18121)
No full textThis report documents the program and the outcomes of Dagstuhl Seminar 18121 "Machine Learning and Model Checking Join Forces". This Dagstuhl Seminar brought together researchers working in the fields of machine learning and model checking. It helped to identify new research topics on the one hand and to help with current problems on the other hand
Murder on the mountain: author talk with Peter J. Wosh
No full textAuthor talk by Peter J. Wosh on May 5th, 2022, on his book, "Murder on the Mountain: crime, passion, and punishment in gilded age New Jersey.
Mr. Melvin J. Collier, RWWL AUC, June 2011
No full textThis video is a conversation with Mr. Melvin J. Collier. Mr. Collier talks about his book, "From Mississippi to Africa: A Journey of Discovery". Daniel Le, AUC Woodruff Library, is the interviewer
A Tripartite Post-Recession Rebalancing
No full textIn this latest Advance & Rutgers Report, entitled “A Tripartite Post-Recession Rebalancing,” Dean James W. Hughes and Professor Joseph J. Seneca deliver an incisive assessment of the current market conditions and obstacles in the path of our economic recovery. They offer a statistical cautionary tale that the private and public sector need to hear and acknowledge in order for the economy to make continued progress.This report was published as Issue Paper Number 7, November 2011, in Advance & Rutgers Report
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