"Stochastic Tools in Mathematics and Science" covers basic stochastic
tools used in physics, chemistry, engineering and the life sciences. The
topics covered include conditional expectations, stochastic processes,
Brownian motion and its relation to partial differential equations,
Langevin equations, the Liouville and Fokker-Planck equations, as well
as Markov chain Monte Carlo algorithms, renormalization, basic
statistical mechanics, and generalized Langevin equations and the
Mori-Zwanzig formalism. The applications include sampling algorithms,
data assimilation, prediction from partial data, spectral analysis, and
turbulence. The book is based on lecture notes from a class that has
attracted graduate and advanced undergraduate students from mathematics
and from many other science departments at the University of California,
Berkeley. Each chapter is followed by exercises. The book will be useful
for scientists and engineers working in a wide range of fields and
applications. For this new edition the material has been thoroughly
reorganized and updated, and new sections on scaling, sampling,
filtering and data assimilation, based on recent research, have been
added. There are additional figures and exercises. Review of earlier
edition: "This is an excellent concise textbook which can be used for
self-study by graduate and advanced undergraduate students and as a
recommended textbook for an introductory course on probabilistic tools
in science." Mathematical Reviews, 2006
