02920nmm a2200361 u 4500001001200000003002700012005001700039007002400056008004100080020001800121100002500139245010900164250001700273260004800290300003100338505015900369653005800528653003100586653003200617653004800649653002600697653003200723653003800755653001600793700002700809710003400836041001900870989003600889490003300925856007400958082001001032520151601042EB000365252EBX0100000000000000021830400000000000000.0cr|||||||||||||||||||||130626 ||| eng a97814614698031 aChorin, Alexandre J.00aStochastic Tools in Mathematics and SciencehElektronische Ressourcecby Alexandre J. Chorin, Ole H Hald a3rd ed. 2013 aNew York, NYbSpringer New Yorkc2013, 2013 aXI, 200 pbonline resource0 aPreliminary -- Probability -- Brownian Motion -- Stationary Stochastic Processes -- Statistical Mechanics -- Index -- Time-Dependent Statistical Mechanics aStatistical Physics, Dynamical Systems and Complexity aEngineering Fluid Dynamics aClassical Continuum Physics aProbability Theory and Stochastic Processes aHydraulic engineering aApplications of Mathematics aDistribution (Probability theory) aMathematics1 aHald, Ole H.e[author]2 aSpringerLink (Online service)07aeng2ISO 639-2 bSpringeraSpringer eBooks 2005-0 aTexts in Applied Mathematics uhttp://dx.doi.org/10.1007/978-1-4614-6980-3?nosfx=yxVerlag3Volltext0 a519.2 a"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