Direct and to the point, this book from one of the field's leaders covers Brownian motion and stochastic calculus at the graduate level, and illustrates the use of that theory in various application domains, emphasizing business and economics. The mathematical development is narrowly focused and briskly paced, with many concrete calculations and a minimum of abstract notation. The applications discussed include: the role of reflected Brownian motion as a storage model, queueing model, or inventory model; optimal stopping problems for Brownian motion, including the influential McDonald-Siegel investment model; optimal control of Brownian motion via barrier policies, including optimal control of Brownian storage systems; and Brownian models of dynamic inference, also called Brownian learning models, or Brownian filtering models.

Author: J. Michael Harrison
Binding Type: Hardcover
Publisher: Cambridge University Press
Published: 12/02/2013
Pages: 205
Weight: 0.95lbs
Size: 9.10h x 6.10w x 0.80d
ISBN: 9781107018396

About the Author
Harrison, J. Michael: - J. Michael Harrison has developed and analyzed stochastic models in several different domains related to business, including mathematical finance and processing network theory. His current research is focused on dynamic models of resource sharing, and on the application of stochastic control theory in economics and operations. Professor Harrison has been honored by the Institute for Operations Research and Management Science (INFORMS) with its Expository Writing Award (1998), the Lanchester Prize for best research publication (2001), and the John von Neumann Theory Prize (2004); he was elected to the National Academy of Engineering in 2008. He is a fellow of INFORMS and of the Institute for Mathematical Statistics.