11 edition of Abstraction, Refinement and Proof for Probabilistic Systems (Monographs in Computer Science) found in the catalog.
November 19, 2004
Written in English
|The Physical Object|
|Number of Pages||388|
The Castro-Liskov algorithm (Miguel Castro and Barbara Liskov, Practical Byzantine Fault Tolerance and Proactive Recovery, TOCS  ) intuitively seems like a modification of Paxos  to handle Byzantine failures, using 3n+1 processes instead of 2n+1 to handle n failures. In I realized that a nice way to think about the algorithm is [ ]. Using this Book xiii Acknowledgments xv 1 Introduction 1 Formal methods 1 The CICS experience 2 The Z notation 3 The importance of proof 4 Abstraction 5 2 Propositional Logic 9 Propositional logic 9 Conjunction 10 Disjunction 13 Implication 14 Equivalence 17 Negation 20 Tautologies and.
Assume-Guarantee Abstraction Refinement for Probabilistic Systems. Anvesh Komuravelli, Corina S. Pasareanu, Edmund M. Clarke. In CAV Learning Probabilistic Systems from Tree Samples. Anvesh Komuravelli, Corina S. Pasareanu, Edmund M. Clarke. In . Abstract interpretation can be applied to the systematic construction of methods and effective algorithms to approximate undecidable or very complex problems in computer science such that the semantics, the proof, the static analysis, the verification, the safety and the security of software or hardware computer systems. In particular, abstract.
style of a natural deduction system “a la Prawitz.” Of course, this has been done be-fore (e.g., in van Dalen ) but our presentation has more of a “computer science” ﬂavor which should make it more easily digestible by our intended audience. Using such a proof system, it is easy to describe very clearly what is a proof by contra-. In the form of tools like EasyCrypt, relational program logics have been used for mechanizing formal proofs of various cryptographic constructions. With an eye towards scaling these successes towards end-to-end security proofs for implementations of distributed systems, we present rF*, a new extension of F*, a general-purpose higher-order stateful programming language with a verification.
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Abstraction, Refinement and Proof for Probabilistic Systems presents a rigorous approach to modeling and reasoning about computer systems that incorporate probability.
Its foundations lie in traditional Boolean sequential-program logic—but its extension to numeric rather than merely true-or-false judgments takes it much further, into areas. McIver A and Morgan C () Abstraction and refinement in probabilistic systems, ACM SIGMETRICS Performance Evaluation Review,(), Online publication date: 1-Mar Baier C, Ciesinski F and Groesser M Quantitative analysis of distributed randomized protocols Proceedings of the 10th international workshop on Formal methods for.
Abstraction Abstraction refinement in probabilistic systems Article (PDF Available) in ACM SIGMETRICS Performance Evaluation Review 32(4) March with 34 Reads How we measure 'reads'.
This is a selection, reworked, expanded and with uniform notation, of our principal published papers since on probabilistic systems and semantics. Its three parts are intended for advanced undergraduates (formal methods), early graduates (semantic techniques) and researchers (new areas) respectively.
Abstraction, refinement and proof for probabilistic systems. A tutorial and reference guide to the principal publications. Springer Verlag Monographs in Computer Science Published January. Abstraction, Refinement and Proof for Probabilistic Systems. and reviews and articles on the book that appeared at the time of its original publication in the New York Times, tthe American.
Abstract. We present a methodology and implementation for Abstraction ANSI-C programs that exhibit probabilistic behaviour, such as failures or randomisation.
We use abstraction-refinement techniques that represent probabilistic programs as Markov decision processes and their abstractions as stochastic two-player games. She has published widely in formal methods on topics ranging from highly theoretical (quantitative modal algebra) to extremely practical (automatic correctness verifiers for probabilistic systems).
Her (joint) text book “Abstraction, refinement and proof for probabilistic systems” is the only full research text on probabilistic program. Abstraction, refinement and proof for probabilistic systems. Annabelle McIver, Carroll Morgan. DR96 Extends conventional data-refinement techniques to MMSS94b.
Probabilistic data refinement. Probabilistic Systems Group. DC96 Treats termination of Rabin's algorithm using the techniques of.
Annabelle McIver and Carroll Morgan. Abstraction, Refinement and Proof for Probabilistic Systems. Monographs in Computer Science. Springer,  Larissa Meinicke and Ian J. Hayes. Algebraic reasoning for probabilistic action systems and while-loops.
Abstract: Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible.
To overcome this challenge, we propose a method called LAR to automatically ‘verify’ such complex systems through a combination of learning, abstraction and refinement from a set of system.
Abstraction, Refinement and Proof for Probabilistic Systems (Monographs in Computer Science) Home ; Abstraction, Refinement and Proof for Probabilistic Systems. Get this from a library. Abstraction, refinement and proof for probabilistic systems.
[Annabelle McIver; Carroll Morgan] -- "Abstraction, Refinement and Proof for Probabilistic Systems presents a rigorous approach to modeling and reasoning about computer systems that incorporate probability. Its foundations lie in. This book summarizes recent research on abstraction techniques for model checking large digital system.
Considering the size of today's digital systems and the capacity of state-of-the-art verification algorithms, abstraction is the only viable solution for the successful application of model checking techniques to industrial-scale designs.
Get this from a library. Abstraction, refinement and proof for probabilistic systems. [Annabelle McIver; Carroll Morgan] -- Provides an integrated coverage of random/probabilistic algorithms, assertion-based program reasoning, and refinement programming models, providing a focused survey on probabilistic program.
She is co-author of the book “Abstraction, Refinement and Proof for Probabilistic Systems”, and of the forthcoming title "The Science of Quantitative Information Flow”. Display in different time zone.
Her research uses mathematics to prove quantitative properties of programs, and more recently to provide foundations for quantitative information flow for analysing security properties.
She is co-author of the book Abstraction, Refinement and Proof for Probabilistic Systems, and of the forthcoming title The Science of Quantitative Information Flow.
A corresponding notion of refinement and simulation-based proof rules are introduced. Probabilistic action systems are based on discrete-time Markov decision processes. Numerical methods solving the optimisation problems posed by Markov decision processes are well-known, and used in a software tool that we have developed.
Refinement checking answers the question on whether an implementation model is a refinement of a specification model, which is of great value for system verification. Some refinement relationships, e.g., trace refinement and failures/divergence refinement, have been recognized for different verification purposes.
In general, refinement checking algorithms often rely on subset construction. > Modern Digital and Analog Communication Systems by B. Lathi > Probability, Random Variables and Stochastic Processes with Errata, > 4ed, Papoulis > Electronic Circuit Analysis and Design,2ed,by Donald A. Neamen > Analysis and Design of Analog Integrated Circuits,4ed, by.
We develop a formal probabilistic automaton model of differential privacy for systems by adapting prior work on differential privacy for functions. The main technical result of the paper is a sound proof technique based on a form of probabilistic bisimulation relation for proving that a system modeled as a probabilistic automaton satisfies.Probability is widely used in the design and analysis of software and hardware systems, as a means to derive e?cient algorithms (e.g.
randomization), as a model for unreliable or unpredictable behavior (as in the study of fault-tolerant systems and computer networks), and as a tool to study performance and - pendability properties.The goal of this book is to provide a comprehensive and systematic introduction to the important and highly applicable method of data refinement and the simulation methods used for proving its correctness.
() Abstract effects and proof-relevant logical relations, ACM SIGPLAN Data Refinement with Probability in Mind, Electronic Notes.