@inproceedings{2fdaf180b2864076b1c0dbeeb76e7b92,
title = "PSense: Automatic Sensitivity Analysis for Probabilistic Programs",
abstract = "PSense is a novel system for sensitivity analysis of probabilistic programs. It computes the impact that a noise in the values of the parameters of the prior distributions and the data have on the program{\textquoteright}s result. PSense relates the program executions with and without noise using a developer-provided sensitivity metric. PSense calculates the impact as a set of symbolic functions of each noise variable and supports various non-linear sensitivity metrics. Our evaluation on 66 programs from the literature and five common sensitivity metrics demonstrates the effectiveness of PSense.",
author = "Zixin Huang and Zhenbang Wang and Sasa Misailovic",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 16th International Symposium on Automated Technology for Verification and Analysis, ATVA 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
year = "2018",
doi = "10.1007/978-3-030-01090-4_23",
language = "English (US)",
isbn = "9783030010898",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "387--403",
editor = "Chao Wang and Lahiri, {Shuvendu K.}",
booktitle = "Automated Technology for Verification and Analysis - 16th International Symposium, ATVA 2018, Proceedings",
address = "Germany",
}