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The correct by construction paradigm is an important component of modern Formal Methods, and here we use the probabilistic Guarded-Command Language $mathit{pGCL}$ to illustrate its application to $mathit{probabilistic}$ programming. $mathit{pGCL}$ extends Dijkstras guarded-command language $mathit{GCL}$ with probabilistic choice, and is equipped with a correctness-preserving refinement relation $(sqsubseteq)$ that enables compact, abstract specifications of probabilistic properties to be transformed gradually to concrete, executable code by applying mathematical insights in a systematic and layered way. Characteristically for correctness by construction, as far as possible the reasoning in each refinement-step layer does not depend on earlier layers, and does not affect later ones. We demonstrate the technique by deriving a fair-coin implementation of any given discrete probability distribution. In the special case of simulating a fair die, our correct-by-construction algorithm turns out to be within spitting distance of Knuth and Yaos optimal solution.
We present a method for verifying the correctness of imperative programs which is based on the automated transformation of their specifications. Given a program prog, we consider a partial correctness specification of the form ${varphi}$ prog ${psi}$
We study weakest precondition reasoning about the (co)variance of outcomes and the variance of run-times of probabilistic programs with conditioning. For outcomes, we show that approximating (co)variances is computationally more difficult than approx
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 GF-transformer semantics for probabilistic while-p
This paper presents a novel method for the automated synthesis of probabilistic programs. The starting point is a program sketch representing a finite family of finite-state Markov chains with related but distinct topologies, and a PCTL specification
Extending our own and others earlier approaches to reasoning about termination of probabilistic programs, we propose and prove a new rule for termination with probability one, also known as almost-certain termination. The rule uses both (non-strict)