Das Anti-doping-Gesetz / / herausgegeben von Prof. Dr. Bernhard Pfister ; mit Beiträgen von Afred Bindels, Martin Heger und Bernhard Pfister

• Record Nr.: UNINA9910162997003321
• Pubbl/distr/stampa: Stuttgart, [Germany] : , : iBoorberg, , 2016; ©2016
• ISBN: 3-415-05891-3
• Descrizione fisica: 1 online resource (71 pages)
• Collana: Recht und Sport ; ; 46
• Disciplina: 344.04233
• Soggetti: Drugs - Law and legislation
• Lingua di pubblicazione: Tedesco
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Data-driven generation of policies / / Austin Parker [and three others]

• Record Nr.: UNINA9910298563603321
• Autore: Parker Austin
• Pubbl/distr/stampa: New York : , : Springer, , 2014
• ISBN: 1-4939-0274-1
• Edizione: [1st ed. 2014.]
• Descrizione fisica: 1 online resource (x, 50 pages) : illustrations
• Collana: SpringerBriefs in Computer Science, , 2191-5768
• Disciplina: 004; 005.55
• Soggetti: Computer algorithms; Artificial intelligence
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "ISSN: 2191-5768."
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Introduction and Related Work -- Optimal State Change Attempts -- Different Kinds of Effect Estimators -- A Comparison with Planning under Uncertainty -- Experimental Evaluation -- Conclusions.
• Sommario/riassunto: This Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.

Mathematics of Epidemics on Networks : From Exact to Approximate Models / / by István Z. Kiss, Joel C. Miller, Péter L. Simon

• Record Nr.: UNINA9910254279903321
• Autore: Kiss István Z
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
• ISBN: 3-319-50806-7
• Edizione: [1st ed. 2017.]
• Descrizione fisica: 1 online resource (xviii, 413 pages) : illustrations
• Collana: Interdisciplinary Applied Mathematics, , 2196-9973 ; ; 46
• Disciplina: 614.4
• Soggetti: Biomathematics; Dynamics; Graph theory; Epidemiology; Probabilities; Mathematical and Computational Biology; Dynamical Systems; Graph Theory; Probability Theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: Preface -- Introduction to Networks and Diseases -- Exact Propagation Models: Top Down -- Exact Propagation Models: Bottom-Up -- Mean-Field Approximations for Heterogeneous Networks -- Percolation-Based Approaches for Disease Modelling -- Hierarchies of SIR Models -- Dynamic and Adaptive Networks -- Non-Markovian Epidemics -- PDE Limits for Large Networks -- Disease Spread in Networks with Large-scale structure -- Appendix: Stochastic Simulation -- Index.
• Sommario/riassunto: This textbook provides an exciting new addition to the area of network science featuring a stronger and more methodical link of models to their mathematical origin and explains how these relate to each other with special focus on epidemic spread on networks. The content of the book is at the interface of graph theory, stochastic processes and dynamical systems. The authors set out to make a significant contribution to closing the gap between model development and the ; supporting mathematics. This is done by: Summarising and presenting the state-of-the-art in modeling epidemics on networks with results and readily usable models signposted throughout the book; Presenting different mathematical approaches to formulate exact and solvable models; Identifying the concrete links between approximate models and their rigorous mathematical representation; Presenting a model hierarchy and clearly highlighting the links between model assumptions and model complexity; Providing a reference source for advanced undergraduate students, as well as doctoral students, postdoctoral researchers and academic experts who are engaged in modeling stochastic processes on networks; Providing software that can solve the differential equation models or directly simulate epidemics in networks. Replete with numerous diagrams, examples, instructive exercises, and online access to simulation algorithms and readily usable code, this book will appeal to a wide spectrum of readers from different backgrounds and academic levels. Appropriate for students with or without a strong background in mathematics, this textbook can form the basis of an advanced undergraduate or graduate course in both mathematics and biology departments alike. .