Abstract interpretation and application to logic programs
Journal of Logic Programming
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
An abstract Monte-Carlo method for the analysis of probabilistic programs
POPL '01 Proceedings of the 28th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Backwards Abstract Interpretation of Probabilistic Programs
ESOP '01 Proceedings of the 10th European Symposium on Programming Languages and Systems
Symbolic Model Checking of Probabilistic Processes Using MTBDDs and the Kronecker Representation
TACAS '00 Proceedings of the 6th International Conference on Tools and Algorithms for Construction and Analysis of Systems: Held as Part of the European Joint Conferences on the Theory and Practice of Software, ETAPS 2000
An Efficient Stochastic Method for Round-Off Error Analysis
Proceedings of the Symposium on Accurate Scientific Computations
Abstract Interpretation of Probabilistic Semantics
SAS '00 Proceedings of the 7th International Symposium on Static Analysis
Static Analyses of the Precision of Floating-Point Operations
SAS '01 Proceedings of the 8th International Symposium on Static Analysis
Semantics of probabilistic programs
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
Abstract interpretation of programs as Markov decision processes
Science of Computer Programming - Special issue: Static analysis symposium (SAS 2003)
Testing randomized software by means of statistical hypothesis tests
Fourth international workshop on Software quality assurance: in conjunction with the 6th ESEC/FSE joint meeting
Abstract interpretation of programs as Markov decision processes
SAS'03 Proceedings of the 10th international conference on Static analysis
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We consider semantics of infinite-state programs, both probabilistic and nondeterministic, as expectation functions: for any set of states A, we associate to each program point a function mapping each state to its expectation of starting a trace reaching A. We then compute a safe upper approximation of these functions using abstract interpretation. This computation takes place in an abstract domain of extended Gaussian (normal) distributions.