Hypothesis testing for rare-event simulation: limitations and possibilities

Daniel Reijsbergen, Pieter-Tjerk de Boer, Werner Scheinhardt

Research output: Chapter in Book/Report/Conference proceedingConference contribution


One of the main applications of probabilistic model checking is to decide whether the probability of a property of interest is above or below a threshold. Using statistical model checking (SMC), this is done using a combination of stochastic simulation and statistical hypothesis testing. When the probability of interest is very small, one may need to resort to rare-event simulation techniques, in particular importance sampling (IS). However, IS simulation does not yield 0/1-outcomes, as assumed by the hypothesis tests commonly used in SMC, but likelihood ratios that are typically close to zero, but which may also take large values.
In this paper we consider two possible ways of combining IS and SMC. One involves a classical IS-scheme from the rare-event simulation literature that yields likelihood ratios with bounded support when applied to a certain (nontrivial) class of models. The other involves a particular hypothesis testing scheme that does not require a-priori knowledge about the samples, only that their variance is estimated well.
Original languageEnglish
Title of host publication7th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2016)
Place of PublicationCorfu, Greece
PublisherSpringer, Cham
Number of pages11
ISBN (Electronic)978-3-319-47166-2
ISBN (Print)978-3-319-47165-5
Publication statusPublished - 5 Oct 2016
EventISoLA 2016 - Corfu, Greece
Duration: 5 Oct 201614 Oct 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
ISSN (Print)0302-9743


ConferenceISoLA 2016
Abbreviated titleISoLA 2016
Internet address


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