
Combining nondeterminism, probability, and termination: equational and metric reasoning
We study monads resulting from the combination of nondeterministic and p...
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Scaling Exact Inference for Discrete Probabilistic Programs
Probabilistic programming languages (PPLs) are an expressive means of re...
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Proving AlmostSure Termination of Probabilistic Programs via Incremental Pruning
The extension of classical imperative programs with realvalued random v...
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An Application of Computable Distributions to the Semantics of Probabilistic Programs
In this chapter, we explore how (Type2) computable distributions can be...
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Ssemantics – an example
The ssemantics makes it possible to explicitly deal with variables in p...
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Probabilistic Agent Programs
Agents are small programs that autonomously take actions based on change...
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Declarative Statistical Modeling with Datalog
Formalisms for specifying statistical models, such as probabilisticprog...
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Generating Functions for Probabilistic Programs
This paper investigates the usage of generating functions (GFs) encoding measures over the program variables for reasoning about discrete probabilistic programs. To that end, we define a denotational GFtransformer semantics for probabilistic whileprograms, and show that it instantiates Kozen's seminal distribution transformer semantics. We then study the effective usage of GFs for program analysis. We show that finitely expressible GFs enable checking superinvariants by means of computer algebra tools, and that they can be used to determine termination probabilities. The paper concludes by characterizing a class of – possibly infinitestate – programs whose semantics is a rational GF encoding a discrete phasetype distribution.
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