Fig 9 - available via license: Creative Commons Attribution-ShareAlike 4.0 International
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The CoinFlips program along with a heatmap showing the frequency counts for each of the 16 abstract paths defined by Γ.
Source publication
We propose a symbolic execution method for programs that can draw random samples. In contrast to existing work, our method can verify randomized programs with unknown inputs and can prove probabilistic properties that universally quantify over all possible inputs. Our technique augments standard symbolic execution with a new class of \emph{probabil...
Contexts in source publication
Context 1
... To illustrate, consider the program in Figure 9a. This program generates samples from two biased coin-flip distributions three times each and returns the total number of heads. ...
Context 2
... í µí°´3µí°´3 } be a set of input variable classes, í µí°µ = {í µí°µ 0 , . . . , í µí°µ 3 } be a set of probabilistic variable classes, and í µí±¡ í µí± 1 and í µí±¡ í µí± 2 be the corresponding random samples of iteration í µí± of Figure 9a, where 1 ≤ í µí± ≤ 3. Each input variable class corresponds to a set of assignments to the input variables, and each probabilistic variable class similarly corresponds to a set of assignment to the probabilistic variables. ...
