000 03781nam a22005775i 4500
001 978-1-4020-5953-7
003 DE-He213
005 20181204132642.0
007 cr nn 008mamaa
008 100301s2007 ne | s |||| 0|eng d
020 _a9781402059537
_9978-1-4020-5953-7
024 7 _a10.1007/978-1-4020-5953-7
_2doi
040 _aISI Library, Kolkata
050 4 _aQA273.A1-274.9
050 4 _aQA274-274.9
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aPBT
_2thema
072 7 _aPBWL
_2thema
082 0 4 _a519.2
_223
100 1 _aAllen, E.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aModeling with Itô Stochastic Differential Equations
_h[electronic resource] /
_cby E. Allen.
264 1 _aDordrecht :
_bSpringer Netherlands,
_c2007.
300 _aXII, 230 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMathematical Modelling: Theory and Applications,
_x1386-2960 ;
_v22
505 0 _aRandom Variables -- Stochastic Processes -- Stochastic Integration -- Stochastic Differential Equations -- Modeling.
520 _aDynamical systems with random influences occur throughout the physical, biological, and social sciences. By carefully studying a randomly varying system over a small time interval, a discrete stochastic process model can be constructed. Next, letting the time interval shrink to zero, an Ito stochastic differential equation model for the dynamical system is obtained. This modeling procedure is thoroughly explained and illustrated for randomly varying systems in population biology, chemistry, physics, engineering, and finance. Introductory chapters present the fundamental concepts of random variables, stochastic processes, stochastic integration, and stochastic differential equations. These concepts are explained in a Hilbert space setting which unifies and simplifies the presentation. Computer programs, given throughout the text, are useful in solving representative stochastic problems. Analytical and computational exercises are provided in each chapter that complement the material in the text. Modeling with Itô Stochastic Differential Equations is useful for researchers and graduate students. As a textbook for a graduate course, prerequisites include probability theory, differential equations, intermediate analysis, and some knowledge of scientific programming.
650 0 _aDistribution (Probability theory.
650 0 _aMathematics.
650 0 _aGlobal analysis (Mathematics).
650 0 _aComputer science
_xMathematics.
650 1 4 _aProbability Theory and Stochastic Processes.
_0http://scigraph.springernature.com/things/product-market-codes/M27004
650 2 4 _aApplications of Mathematics.
_0http://scigraph.springernature.com/things/product-market-codes/M13003
650 2 4 _aAnalysis.
_0http://scigraph.springernature.com/things/product-market-codes/M12007
650 2 4 _aMathematical Modeling and Industrial Mathematics.
_0http://scigraph.springernature.com/things/product-market-codes/M14068
650 2 4 _aComputational Mathematics and Numerical Analysis.
_0http://scigraph.springernature.com/things/product-market-codes/M1400X
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9789048174874
776 0 8 _iPrinted edition:
_z9789048112944
776 0 8 _iPrinted edition:
_z9781402059520
830 0 _aMathematical Modelling: Theory and Applications,
_x1386-2960 ;
_v22
856 4 0 _uhttps://doi.org/10.1007/978-1-4020-5953-7
912 _aZDB-2-SMA
942 _cEB
950 _aMathematics and Statistics (Springer-11649)
999 _c425448
_d425448