LEADER 03670nam  22005775  450 
001 9910349346003321
005 20201222013238.0
010   $a1-4939-9579-0
024 7 $a10.1007/978-1-4939-9579-0
035   $a(CKB)4100000008959011
035   $a(MiAaPQ)EBC5849489
035   $a(DE-He213)978-1-4939-9579-0
035   $a(PPN)242821553
035   $a(EXLCZ)994100000008959011
100   $a20190810d2019      u|             0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAnalysis and Approximation of Rare Events$b[electronic resource] $eRepresentations and Weak Convergence Methods /$fby Amarjit Budhiraja, Paul Dupuis
205   $a1st ed. 2019.
210  1$aNew York, NY :$cSpringer US :$cImprint: Springer,$d2019.
215   $a1 online resource (577 pages)
225 1 $aProbability Theory and Stochastic Modelling,$x2199-3130 ;$v94
311   $a1-4939-9577-4 
320   $aIncludes bibliographical references and index.
327   $aPreliminaries and elementary examples -- Discrete time processes -- Continuous time processes -- Monte Carlo approximation.
330   $aThis book presents broadly applicable methods for the large deviation and moderate deviation analysis of discrete and continuous time stochastic systems. A feature of the book is the systematic use of variational representations for quantities of interest such as normalized logarithms of probabilities and expected values. By characterizing a large deviation principle in terms of Laplace asymptotics, one converts the proof of large deviation limits into the convergence of variational representations. These features are illustrated though their application to a broad range of discrete and continuous time models, including stochastic partial differential equations, processes with discontinuous statistics, occupancy models, and many others. The tools used in the large deviation analysis also turn out to be useful in understanding Monte Carlo schemes for the numerical approximation of the same probabilities and expected values. This connection is illustrated through the design and analysis of importance sampling and splitting schemes for rare event estimation. The book assumes a solid background in weak convergence of probability measures and stochastic analysis, and is suitable for advanced graduate students, postdocs and researchers.
410  0$aProbability Theory and Stochastic Modelling,$x2199-3130 ;$v94
606   $aProbabilities
606   $aApplied mathematics
606   $aEngineering mathematics
606   $aNumerical analysis
606   $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004
606   $aMathematical and Computational Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11006
606   $aNumerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M14050
615  0$aProbabilities.
615  0$aApplied mathematics.
615  0$aEngineering mathematics.
615  0$aNumerical analysis.
615 14$aProbability Theory and Stochastic Processes.
615 24$aMathematical and Computational Engineering.
615 24$aNumerical Analysis.
676   $a511.4
700   $aBudhiraja$b Amarjit$4aut$4http://id.loc.gov/vocabulary/relators/aut$0782120
702   $aDupuis$b Paul$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910349346003321
996   $aAnalysis and Approximation of Rare Events$92513905
997   $aUNINA