Titlebook: Domain-Specific Languages; IFIP TC 2 Working Co Walid Mohamed Taha Conference proceedings 2009 IFIP International Federation for Informatio

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变态

Rosalba D’Onofrio,Elio Trusianior of explanations, we identify generic constructs for building stories out of events, and obtaining explanations by applying stories to specific examples. These generic constructs are then adapted to the particular explanation domain of probabilistic reasoning. Finally, we develop a visual notation for explaining probabilistic reasoning.

Feigned

Rosalba D’Onofrio,Elio Trusianiterpretive overhead on deterministic parts of a model. We thus take advantage of the existing OCaml implementation to achieve competitive performance and ease of use. Inference algorithms can easily be embedded in probabilistic programs themselves.

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雄伟

Embedded Probabilistic Programmingterpretive overhead on deterministic parts of a model. We thus take advantage of the existing OCaml implementation to achieve competitive performance and ease of use. Inference algorithms can easily be embedded in probabilistic programs themselves.

unstable-angina

Operator Language: A Program Generation Framework for Fast Kernelsewriting systems, a structural architecture model and empirical search, we automatically generate very fast C implementations for state-of-the-art multicore CPUs that rival hand-tuned implementations.

GEM

Model-Driven Engineering from Modular Monadic Semantics: Implementation Techniques Targeting Hardwarmentations? This paper addresses this question and presents an overview of techniques for compiling monadic concurrency models directly into reasonably efficient software and hardware implementations. The implementation techniques described in this article form the basis of a semantics-directed approach to model-driven engineering.