LEADER 01499nam0-22005171i-450 001 990000367440403321 005 20180524121044.0 010 $a0-521-43064-X 035 $a000036744 035 $aFED01000036744 035 $a(Aleph)000036744FED01 035 $a000036744 100 $a20020821d1992----km-y0itay50------ba 101 0 $aeng 102 $aGB 105 $ay-------001yy 200 1 $aNumerical recipes in Fortran$ethe art of scientific computing$fWilliam H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery 205 $a2nd ed. 210 $aCambridge$cCambridge University Press$dc1992 215 $aXXVI, 963 p.$d24 cm 610 0 $aInformatica 610 0 $aAnalisi numerica$aElaborazione dati 610 0 $aStatistica$aElaborazione dati 676 $a519.4 702 1$aFlannery,$bBrian P. 702 1$aPress,$bWilliam H. 702 1$aTeukolsky,$bSaul A. 702 1$aVetterling,$bWilliam T. 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000367440403321 952 $a04 013-260$bIRC 1826/L$fDINCH 952 $a04 013-262$bDIC 1506$fDINCH 952 $a03 INF.0,13$b666$fIINTC 952 $a07 D-141$b5016/444$fDINSC 952 $a124-I-22$b12140$fMA1 952 $a15 ID K/4-74$fDINID 959 $aDINCH 959 $aIINTC 959 $aDINSC 959 $aMA1 959 $aDINID 962 $a65-00 962 $a62-04 996 $aNumerical recipes in Fortran$9126561 997 $aUNINA LEADER 05494nam 2201261 450 001 9910823944403321 005 20200520144314.0 010 $a1-4008-4063-5 024 7 $a10.1515/9781400840632 035 $a(CKB)2670000000139868 035 $a(EBL)832068 035 $a(OCoLC)769927219 035 $a(SSID)ssj0000593432 035 $a(PQKBManifestationID)11381906 035 $a(PQKBTitleCode)TC0000593432 035 $a(PQKBWorkID)10741466 035 $a(PQKB)10613464 035 $a(StDuBDS)EDZ0000515072 035 $a(DE-B1597)448013 035 $a(OCoLC)979582576 035 $a(DE-B1597)9781400840632 035 $a(Au-PeEL)EBL832068 035 $a(CaPaEBR)ebr10862955 035 $a(CaONFJC)MIL597392 035 $a(MiAaPQ)EBC832068 035 $a(EXLCZ)992670000000139868 100 $a20110908h20122012 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSpatiotemporal data analysis /$fGidon Eshel 205 $aCourse Book 210 1$aPrinceton :$cPrinceton University Press,$d[2012] 210 4$dİ2012 215 $a1 online resource (336 p.) 300 $aDescription based upon print version of record. 311 $a0-691-12891-X 320 $aIncludes bibliographical references and index. 327 $t Frontmatter -- $tContents -- $tPreface -- $tAcknowledgments -- $tPart 1. Foundations -- $tOne. Introduction and Motivation -- $tTwo. Notation and Basic Operations -- $tThree. Matrix Properties, Fundamental Spaces, Orthogonality -- $tFour. Introduction to Eigenanalysis -- $tFive. The Algebraic Operation of SVD -- $tPart 2. Methods of Data Analysis -- $tSix. The Gray World of Practical Data Analysis: An Introduction to Part 2 -- $tSeven. Statistics in Deterministic Sciences: An Introduction -- $tEight. Autocorrelation -- $tNine. Regression and Least Squares -- $tTen. The Fundamental Theorem of Linear Algebra -- $tEleven. Empirical Orthogonal Functions -- $tTwelve. The SVD Analysis of Two Fields -- $tThirteen. Suggested Homework -- $tIndex 330 $a"A severe thunderstorm morphs into a tornado that cuts a swath of destruction through Oklahoma. How do we study the storm's mutation into a deadly twister? Avian flu cases are reported in China. How do we characterize the spread of the flu, potentially preventing an epidemic? The way to answer important questions like these is to analyze the spatial and temporal characteristics--origin, rates, and frequencies--of these phenomena. This comprehensive text introduces advanced undergraduate students, graduate students, and researchers to the statistical and algebraic methods used to analyze spatiotemporal data in a range of fields, including climate science, geophysics, ecology, astrophysics, and medicine. Gidon Eshel begins with a concise yet detailed primer on linear algebra, providing readers with the mathematical foundations needed for data analysis. He then fully explains the theory and methods for analyzing spatiotemporal data, guiding readers from the basics to the most advanced applications. This self-contained, practical guide to the analysis of multidimensional data sets features a wealth of real-world examples as well as sample homework exercises and suggested exams"--$cProvided by publisher. 606 $aSpatial analysis (Statistics) 610 $aEOF analysis. 610 $aEOF. 610 $aGram?chmidt orthogonalization. 610 $aSVD analysis. 610 $aSVD. 610 $aastrophysics. 610 $aautocorrelation functions. 610 $aautocovariance. 610 $aautoregressive model. 610 $aclimate science. 610 $acolumn space. 610 $acovariability matrix. 610 $adata analysis. 610 $adata matrices. 610 $adegrees of freedom. 610 $adeterministic science. 610 $aecology. 610 $aeigen-decomposition. 610 $aeigen-techniques. 610 $aeigenanalysis. 610 $aeigenvalues. 610 $aempirical orthogonal functions. 610 $aempirical science. 610 $aempiricism. 610 $aexercises. 610 $aforward problem. 610 $ageophysics. 610 $ainverse problem. 610 $alinear algebra. 610 $alinear regression. 610 $amatrices. 610 $amatrix structure. 610 $amatrix. 610 $amedicine. 610 $amultidimensional data sets. 610 $amultidimensional data. 610 $anondeterministic phenomena. 610 $anull space. 610 $aphenomena. 610 $aprobability distribution. 610 $arow space. 610 $asingular value decomposition. 610 $aspatiotemporal data. 610 $aspectral representation. 610 $asquare matrices. 610 $astatistics. 610 $astochastic processes. 610 $asubjective decisions. 610 $atheoretical science. 610 $atime series. 610 $atimescale. 610 $atornado. 610 $avariables. 610 $avectors. 615 0$aSpatial analysis (Statistics) 676 $a519.5/36 686 $aSCI019000$aMAT002050$2bisacsh 700 $aEshel$b Gidon$f1958-$01678986 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910823944403321 996 $aSpatiotemporal data analysis$94046958 997 $aUNINA