Probabilistic Output Analyses for Deterministic Programs — Reusing Existing Non-probabilistic Analyses

01/20/2020
by   Maja Hanne Kirkeby, et al.
0

We consider reusing established non-probabilistic output analyses (either forward or backwards) that yield over-approximations of a program's pre-image or image relation, e.g., interval analyses. We assume a probability measure over the program input and present two techniques (one for forward and one for backward analyses) that both derive upper and lower probability bounds for the output events. We demonstrate the most involved technique, namely the forward technique, for two examples and compare their results to a cutting-edge probabilistic output analysis.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/05/2017

Combining Forward and Backward Abstract Interpretation of Horn Clauses

Alternation of forward and backward analyses is a standard technique in ...
07/12/2019

Verified Self-Explaining Computation

Common programming tools, like compilers, debuggers, and IDEs, crucially...
07/30/2019

Computing Abstract Distances in Logic Programs

Abstract interpretation is a well-established technique for performing s...
11/18/2019

Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs (Technical Report)

State-of-the-art inference approaches in probabilistic logic programming...
04/06/2022

Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming

We propose a new method to approximate the posterior distribution of pro...
04/14/2022

This is the Moment for Probabilistic Loops

We present a novel static analysis technique to derive higher moments fo...
01/22/2015

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm ...