Risk - A Multidisciplinary Introduction
Claudia Klüppelberg, Daniel Straub, Isabell M. Welpe
Springer, Jun 10, 2014 - Mathematics - 476 pages
This is a unique book addressing the integration of risk methodology from various fields. It will stimulate intellectual debate and communication across disciplines, promote better risk management practices and contribute to the development of risk management methodologies. Individual chapters explain fundamental risk models and measurement, and address risk and security issues from diverse areas such as finance and insurance, the health sciences, life sciences, engineering and information science. Integrated Risk Sciences is an emerging discipline that considers risks in different fields, aiming at a common language, and at sharing and improving methods developed in different fields. Readers should have a Bachelor degree and have taken at least one basic university course in statistics and probability. The main goal of the book is to provide basic knowledge on risk and security in a common language; the authors have taken particular care to ensure that all content can readily be understood by doctoral students and researchers across disciplines. Each chapter provides simple case studies and examples, open research questions and discussion points, and a selected bibliography inviting readers to further study.
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applications approach assume Bayesian behavior Black–Scholes calculated cervical cancer coherent risk measure complex components consider convex copula correlation cost-benefit analysis costs covariance decision theory defined density developed dynamics economic effects engineering estimated ethics evaluation example expected utility extreme events extreme value theory failure genetic Hence HPV testing human Illustration individual interaction interfaces Klüppelberg linear losses Markov chain mathematical matrix mean methods model risk monetary risk measures multi-touch Multidisciplinary Introduction München normal distribution observations optimal options outcomes parameter PCPTRC portfolio posterior prediction probabilistic problem project risks prostate cancer quantitative random variables regression risk analysis risk assessment risk factors risk management risk model risk research safety sample screening Sect sensor SNP markers statistical stochastic Straub SysML Technische Universität München Theorem tion uncertainty utility function Value-at-Risk variance vector