Prinsys (pronounced "princess") is a new software-tool for probabilistic invariant synthesis. In this paper we discuss its implementation and improvements of the methodology which was set out in previous work. In particular we have substantially simplified the method and generalised it to non-linear programs and invariants. Prinsys follows a constraint-based approach. A given parameterised loop annotation is speculatively placed in the program. The tool returns a formula that captures precisely the invariant instances of the given candidate. Our approach is sound and complete. Prinsys's applicability is evaluated on several examples. We believe the tool contributes to the successful analysis of sequential probabilistic programs with infinit...
Probabilistic predicate transformers provide a semantics for imperative programs containing both dem...
This thesis describes work on two applications of probabilistic programming: the learning of probab...
The notion of program sensitivity (aka Lipschitz continuity) specifies that changes in the program i...
"A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in the ...
One of the main challenges in the analysis of probabilistic programs is to compute invariant propert...
Morgan and McIver's weakest pre-expectation framework is one of the most well-established methods fo...
Abstract. We present static analyses for probabilistic loops using expectation in-variants. Probabil...
We present a novel static analysis technique to derive higher moments for program variables for a la...
International audienceBy combining algorithmic learning, decision procedures, and predicate abstract...
Abstract. By combining algorithmic learning, decision procedures, and predicate abstraction, we pres...
This thesis pursues the synthesis of probabilistic programs with rewards. Probabilistic synthesis le...
Probabilistic predicate transformers provide a semantics for imperative programs containing both dem...
This paper investigates the usage of generating functions (GFs) encoding measures over the program v...
The weakest pre-expectation calculus [20] has been proved to be a mature theory to analyze quan-tita...
Back and von Wright have developed algebraic laws for reasoning about loops in the refinement calcul...
Probabilistic predicate transformers provide a semantics for imperative programs containing both dem...
This thesis describes work on two applications of probabilistic programming: the learning of probab...
The notion of program sensitivity (aka Lipschitz continuity) specifies that changes in the program i...
"A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in the ...
One of the main challenges in the analysis of probabilistic programs is to compute invariant propert...
Morgan and McIver's weakest pre-expectation framework is one of the most well-established methods fo...
Abstract. We present static analyses for probabilistic loops using expectation in-variants. Probabil...
We present a novel static analysis technique to derive higher moments for program variables for a la...
International audienceBy combining algorithmic learning, decision procedures, and predicate abstract...
Abstract. By combining algorithmic learning, decision procedures, and predicate abstraction, we pres...
This thesis pursues the synthesis of probabilistic programs with rewards. Probabilistic synthesis le...
Probabilistic predicate transformers provide a semantics for imperative programs containing both dem...
This paper investigates the usage of generating functions (GFs) encoding measures over the program v...
The weakest pre-expectation calculus [20] has been proved to be a mature theory to analyze quan-tita...
Back and von Wright have developed algebraic laws for reasoning about loops in the refinement calcul...
Probabilistic predicate transformers provide a semantics for imperative programs containing both dem...
This thesis describes work on two applications of probabilistic programming: the learning of probab...
The notion of program sensitivity (aka Lipschitz continuity) specifies that changes in the program i...