In the ninth and tenth centuries, the Vikings created a cultural network that spanned four continents: from the Caspian Sea to the North Atlantic and from the Arctic Circle to the Mediterranean. The Viking Age was a period of major change as a result of the Vikings impact on neighboring areas and the introduction of external influences into Scandinavia. This book explores Viking culture from a global perspective, examining the influences of their varied contacts from around the world and how Viking Scandinavia drew from both Christian Europe and the Islamic world.

The book focuses on the core period of the Viking Age, from the late eighth to the early eleventh centuries. New discoveries by archaeologists and metal detectorists highlight the interconnected nature of the cultures of Europe, Byzantium, and the Middle East.

Vikings accompanies a major exhibition developed jointly by the British Museum, the National Museum of Denmark, and the Museum for Prehistory and Early History in Berlin. Edited by the exhibition curators Gareth Williams, Peter Pentz, and Matthias Wemhoff and with contributions from a number of key experts, the book, with its strong, flowing narrative and integrated illustrations, draws on a wealth of Viking objects to provide a rich and vivid account of the impact of Viking expansion throughout the world."

This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alpha-stable processes. In particular, it covers aspects of characterization via the spectral measure of heavy-tailed distributions and then provides an overview of their applications in wireless communications channel modeling. The final chapter concludes with an overview of analysis for probabilistic spatial percolation methods that are relevant in the modeling of graphical networks and connectivity applications in sensor networks, which also incorporate stochastic geometry features.