Probabilistic Analysis using Theorem Proving: A Higher-order Logic Based Approach

Traditionally, computer simulation techniques areused to perform probabilistic analysis. However, theyprovide less accurate results and cannot handlelarge-scale problems due to their enormous CPU timerequirements. Recently, a significant amount offormalization has been done in higher-order logicthat allows us to conduct precise probabilisticanalysis using theorem proving and thus overcome thelimitations of the simulation based probabilisticanalysis approach. Some major contributions includethe formalization of both discrete and continuousrandom variables and the verification of corresponding probabilistic and statisticalproperties. This book presents a concise descriptionof the infrastructures behind these capabilities andtheir utilization to conduct the probabilisticanalysis of real-world systems. The case studies ofthe round-off error of a digital processor, theCoupon Collector''s problem and the Stop-and-Waitprotocol are used to illustrate the proposed analysisapproach. Designed as an independent research tool, the bookpresents a well-thought-out treatment of a rapidlyemerging multidisciplinary field across Mathematics,Computer Science and Engineering.

Osman Hasan, PhD, is a Postdoctoral Fellow at ConcordiaUniversity. His research interests include Higher-order-logicTheorem Proving and Probabilistic Analysis.Sofiène Tahar, PhD, is a Computer Engineering Professor atConcordia University. His research interests are in FormalVerification of Hardware, Embedded Systems and System-on-Chip.