Stochastic Integration in Banach Spaces Theory and Applications
Considering Poisson random measures as the driving sources for stochastic (partial) differential equations allows us to incorporate jumps and to model sudden, unexpected phenomena. By using such equations the present book introduces a new method for model
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Vidyadhar Mandrekar Barbara Rüdiger
Stochastic Integration in Banach Spaces Theory and Applications
Probability Theory and Stochastic Modelling Volume 73
Editors-in-Chief Søren Asmussen, Aarhus, Denmark Peter W. Glynn, Stanford, CA, USA Thomas G. Kurtz, Madison, WI, USA Yves Le Jan, Orsay, France Advisory Board Joe Gani, Canberra, ACT, Australia Martin Hairer, Coventry, UK Peter Jagers, Gothenburg, Sweden Ioannis Karatzas, New York, NY, USA Frank P. Kelly, Cambridge, UK Andreas E. Kyprianou, Bath, UK Bernt Øksendal, Oslo, Norway George Papanicolaou, Stanford, CA, USA Etienne Pardoux, Marseille, France Edwin Perkins, Vancouver, Canada Halil Mete Soner, Zürich, Switzerland
The Stochastic Modelling and Probability Theory series is a merger and continuation of Springer’s two well established series Stochastic Modelling and Applied Probability and Probability and Its Applications series. It publishes research monographs that make a significant contribution to probability theory or an applications domain in which advanced probability methods are fundamental. Books in this series are expected to follow rigorous mathematical standards, while also displaying the expository quality necessary to make them useful and accessible to advanced students as well as researchers. The series covers all aspects of modern probability theory including • • • • • •
Gaussian processes Markov processes Random Fields, point processes and random sets Random matrices Statistical mechanics and random media Stochastic analysis
as well as applications that include (but are not restricted to): • Branching processes and other models of population growth • Communications and processing networks • Computational methods in probability and stochastic processes, including simulation • Genetics and other stochastic models in biology and the life sciences • Information theory, signal processing, and image synthesis • Mathematical economics and finance • Statistical methods (e.g. empirical processes, MCMC) • Statistics for stochastic processes • Stochastic control • Stochastic models in operations research and stochastic optimization • Stochastic models in the physical sciences
More information about this series at http://www.springer.com/series/13205
Vidyadhar Mandrekar Barbara Rüdiger •
Stochastic Integration in Banach Spaces Theory and Applications
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Vidyadhar Mandrekar Department of Statistics and Probability Michigan State University East Lansing, MI USA
ISSN 2199-3130 ISBN 978-3-319-12852-8 DOI 10.1007/978-3-319-12853-5
Barbara Rüdiger Department of Mathematics and Informatics University of Wuppertal Wuppertal Germany
ISSN 2199-3149 (electronic) ISBN 978-3-319-12853-5 (eBook)
Library of Congress Control Number: 2014954590 Mathematics Subject Classification (2010): 60H15, 60H05, 60G57, 60G51, 91G30, 91G80, 60G35, 35B40 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the mate
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