Quantitative Reasoning
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Quantitative Reasoning
Author: Eric Zaslow
language: en
Publisher: Cambridge University Press
Release Date: 2020-01-16
Employs basic mathematical skills to teach students how to address topical, real-world problems using quantitative reasoning.
Official GRE Quantitative Reasoning Practice Questions
Author: Educational Testing Service
language: en
Publisher: McGraw Hill Professional
Release Date: 2014-08-15
150 REAL GRE Quantitative Reasoning questions--direct from the test maker! The best way to prepare for the Quantitative Reasoning measure of the GRE revised General Test is with real GRE test questions--and that is what you will find in this unique guide! Specially created for you by ETS, it offers 150 actual Quantitative Reasoning questions with complete explanations. Plus, this guide includes a review of math topics likely to appear on the Quantitative Reasoning measure. Only ETS can show you exactly what to expect on the test. So for in-depth practice and accurate test preparation for the Quantitative Reasoning measure, this guide is your best choice! Look inside to find: Real GRE Quantitative Reasoning test questions arranged by content and question type--to help you build your test-taking skills. Plus, mixed practice sets. Answers and explanations for every question! GRE Math Review covering math topics you need to know for the test. ETS's own test-taking strategies: Valuable hints and tips to help you do your best on the test. Official information on the GRE Quantitative Reasoning measure: The facts about the test content, structure, scoring, and more--straight from ETS.
Symbolic Execution and Quantitative Reasoning
Author: Corina S. Păsăreanu
language: en
Publisher: Morgan & Claypool Publishers
Release Date: 2020-05-13
This book reviews recent advances in symbolic execution and its probabilistic variant and discusses how they can be used to ensure the safety and security of software systems. Symbolic execution is a systematic program analysis technique which explores multiple program behaviors all at once by collecting and solving symbolic constraints collected from the branching conditions in the program. The obtained solutions can be used as test inputs that execute feasible program paths. Symbolic execution has found many applications in various domains, such as security, smartphone applications, operating systems, databases, and more recently deep neural networks, uncovering subtle errors and unknown vulnerabilities. We review here the technique has also been extended to reason about algorithmic complexity and resource consumption. Furthermore, symbolic execution has been recently extended with probabilistic reasoning, allowing one to reason about quantitative properties of software systems. The approach computes the conditions to reach target program events of interest and uses model counting to quantify the fraction of the input domain satisfying these conditions thus computing the probability of event occurrence. This probabilistic information can be used for example to compute the reliability of an aircraft controller under different wind conditions (modeled probabilistically) or to quantify the leakage of sensitive data in a software system, using information theory metrics such as Shannon entropy. This book is intended for students and software engineers who are interested in advanced techniques for testing and verifying software systems.