LEADER 01970nam0-2200649---450- 001 990000084420203316 005 20090115133001.0 010 $a88-15-08135-6 035 $a0008442 035 $aUSA010008442 035 $a(ALEPH)000008442USA01 035 $a0008442 100 $a20000914d2001----|||y0itay0103----ba 101 0 $aIta 102 $aIT 105 $a||||||||001yy 200 1 $a<> new economy$fElena Vaciago, Giacomo Vaciago 210 $aBologna$cIl Mulino$d2001 215 $a129 p.$d20 cm 225 2 $aFarsi un'idea$v58 410 0$12001$aFarsi un'idea$v58 606 0 $aInternet $xImpiego nelle aziende 676 $a658.05467 700 1$aVACIAGO,$bElena$089572 701 1$aVACIAGO,$bGiacomo$089573 801 $aIT$bSALBC$gISBD 912 $a990000084420203316 951 $a658.054 VAC 1 (IEP III 687)$b9009 E.C.$cIEP III$d00072352 951 $a300 337 VAC$b9544 DISES 959 $aBK 969 $aECO 969 $aDISES 979 $aTAMI$b40$c20001025$lUSA01$h1549 979 $c20000914$lUSA01$h1738 979 $c20000919$lUSA01$h1048 979 $c20000919$lUSA01$h1521 979 $c20001019$lUSA01$h1056 979 $c20001019$lUSA01$h1454 979 $c20001019$lUSA01$h1501 979 $c20001019$lUSA01$h1539 979 $c20001024$lUSA01$h1515 979 $c20001027$lUSA01$h1519 979 $c20001027$lUSA01$h1523 979 $c20001110$lUSA01$h1710 979 $c20001124$lUSA01$h1208 979 $aCHIARA$b40$c20010405$lUSA01$h1231 979 $aCHIARA$b40$c20010405$lUSA01$h1233 979 $aCHIARA$b40$c20010405$lUSA01$h1346 979 $c20020403$lUSA01$h1616 979 $aPATRY$b90$c20040406$lUSA01$h1607 979 $aRSIAV1$b90$c20090115$lUSA01$h1330 979 $c20121027$lUSA01$h1541 979 $c20121027$lUSA01$h1601 979 $c20121027$lUSA01$h1610 996 $aNew economy$952679 997 $aUNISA DEB $aUSA10467 LEADER 01290nam--2200445---450 001 990002888910203316 005 20180308150910.0 010 $a88-7246-665-2 035 $a000288891 035 $aUSA01000288891 035 $a(ALEPH)000288891USA01 035 $a000288891 100 $a20070327h2005----km-y0itay50------ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $a<> specchio della fantasia$eretorica, magia e scrittura in Giordano Bruno$fMaria Pia Ellero 210 $aLucca$cMaria Pacini Fazzi$dcopyr. 2005 215 $a166 p.$d24 cm 225 2 $aMorgana$v7 410 0$12001$aMorgana 600 1$aBruno,$bGiordano 676 $a195 700 1$aELLERO,$bMaria Pia$0170942 801 0$aIT$bsalbc$gISBD 912 $a990002888910203316 951 $aII.1.B. 232$b195394 L.M.$cII.1.$d00130075 951 $aCC 111.85 ELL$b6959 FIL 959 $aBK 969 $aUMA 969 $aFIL 979 $aPAOLA$b90$c20070327$lUSA01$h1216 979 $aPAOLA$b90$c20070327$lUSA01$h1219 979 $aPAOLA$b90$c20071030$lUSA01$h1123 979 $c20121027$lUSA01$h1552 979 $c20121027$lUSA01$h1606 979 $c20121027$lUSA01$h1611 996 $aSpecchio della fantasia$9989836 997 $aUNISA DEB $aSA0013910 LEADER 04315nam 2200601Ia 450 001 9910467021203321 005 20211018122746.0 010 $a1-4008-3407-4 024 7 $a10.1515/9781400834075 035 $a(CKB)3820000000031620 035 $a(MiAaPQ)EBC557154 035 $a(OCoLC)650305544 035 $a(MdBmJHUP)muse36669 035 $a(WaSeSS)Ind00023649 035 $a(DE-B1597)447021 035 $a(OCoLC)973401881 035 $a(OCoLC)979910843 035 $a(DE-B1597)9781400834075 035 $a(PPN)187954755 035 $a(Au-PeEL)EBL557154 035 $a(CaPaEBR)ebr10397707 035 $a(CaONFJC)MIL264498 035 $a(OCoLC)654029535 035 $a(EXLCZ)993820000000031620 100 $a20080623d2009 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMathematics in India$b[electronic resource] /$fKim Plofker 205 $aCourse Book 210 $aPrinceton $cPrinceton University Press$dc2009 215 $a1 online resource (xi, 357 p. )$cill., map 311 0 $a0-691-12067-6 320 $aIncludes bibliographical references (p. [327]-351) and index. 327 $tFront matter --$tContents --$tPreface --$tList Of Abbreviations --$tChapter 1. Introduction --$tChapter 2. Mathematical Thought in Vedic India --$tChapter 3. Mathematical Traces in the Early Classical Period --$tChapter 4. The Mathematical Universe --$tChapter 5. The Genre of Medieval Mathematics --$tChapter 6. The Development of "Canonical" Mathematics --$tChapter 7. The School of M?dhava in Kerala --$tChapter 8. Exchanges with the Islamic World --$tChapter 9. Continuity and Changes in the Modern Period --$tAppendix A. Some Basic Features of Sanskrit Language and Literature --$tAppendix B. Biographical Data on Indian Mathematicians --$tBibliography --$tIndex 330 $aBased on extensive research in Sanskrit sources, Mathematics in India chronicles the development of mathematical techniques and texts in South Asia from antiquity to the early modern period. Kim Plofker reexamines the few facts about Indian mathematics that have become common knowledge--such as the Indian origin of Arabic numerals--and she sets them in a larger textual and cultural framework. The book details aspects of the subject that have been largely passed over in the past, including the relationships between Indian mathematics and astronomy, and their cross-fertilizations with Islamic scientific traditions. Plofker shows that Indian mathematics appears not as a disconnected set of discoveries, but as a lively, diverse, yet strongly unified discipline, intimately linked to other Indian forms of learning. Far more than in other areas of the history of mathematics, the literature on Indian mathematics reveals huge discrepancies between what researchers generally agree on and what general readers pick up from popular ideas. This book explains with candor the chief controversies causing these discrepancies--both the flaws in many popular claims, and the uncertainties underlying many scholarly conclusions. Supplementing the main narrative are biographical resources for dozens of Indian mathematicians; a guide to key features of Sanskrit for the non-Indologist; and illustrations of manuscripts, inscriptions, and artifacts. Mathematics in India provides a rich and complex understanding of the Indian mathematical tradition. **Author's note: The concept of "computational positivism" in Indian mathematical science, mentioned on p. 120, is due to Prof. Roddam Narasimha and is explored in more detail in some of his works, including "The Indian half of Needham's question: some thoughts on axioms, models, algorithms, and computational positivism" (Interdisciplinary Science Reviews 28, 2003, 1-13). 606 $aMathematics$zIndia$xHIstory 606 $aMathematics$zIndia$vBio-bibliography 608 $aElectronic books. 615 0$aMathematics$xHIstory. 615 0$aMathematics 676 $a510.954 686 $aSG 525$2rvk 700 $aPlofker$b Kim$f1964-$0767892 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910467021203321 996 $aMathematics in India$92452276 997 $aUNINA LEADER 11661nam 2200709 a 450 001 9910141250103321 005 20221108020317.0 010 $a1-283-17797-8 010 $a1-119-99871-9 010 $a1-119-99593-0 010 $a1-119-99592-2 010 $a9786613177971 035 $a(CKB)2670000000112757 035 $a(StDuBDS)AH21634310 035 $a(SSID)ssj0000507242 035 $a(PQKBManifestationID)11307665 035 $a(PQKBTitleCode)TC0000507242 035 $a(PQKBWorkID)10546555 035 $a(PQKB)10113424 035 $a(MiAaPQ)EBC819286 035 $a(MiAaPQ)EBC712124 035 $a(Au-PeEL)EBL712124 035 $a(OCoLC)747314062 035 $a(PPN)170229661 035 $a(EXLCZ)992670000000112757 100 $a20110330d2011 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModeling uncertainty in the earth sciences$b[electronic resource] /$fJef Caers 205 $a1st ed. 210 $aHoboken, N.J. $cWiley$d2011 215 $a1 online resource (240 p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-119-99262-1 311 $a1-119-99263-X 320 $aIncludes bibliographical references and index. 327 $aIntro -- Modeling Uncertainty in the Earth Sciences -- Contents -- Preface -- Acknowledgements -- 1 Introduction -- 1.1 Example Application -- 1.1.1 Description -- 1.1.2 3D Modeling -- 1.2 Modeling Uncertainty -- Further Reading -- 2 Review on Statistical Analysis and Probability Theory -- 2.1 Introduction -- 2.2 Displaying Data with Graphs -- 2.2.1 Histograms -- 2.3 Describing Data with Numbers -- 2.3.1 Measuring the Center -- 2.3.2 Measuring the Spread -- 2.3.3 Standard Deviation and Variance -- 2.3.4 Properties of the Standard Deviation -- 2.3.5 Quantiles and the QQ Plot -- 2.4 Probability -- 2.4.1 Introduction -- 2.4.2 Sample Space, Event, Outcomes -- 2.4.3 Conditional Probability -- 2.4.4 Bayes' Rule -- 2.5 Random Variables -- 2.5.1 Discrete Random Variables -- 2.5.2 Continuous Random Variables -- 2.5.2.1 Probability Density Function (pdf) -- 2.5.2.2 Cumulative Distribution Function -- 2.5.3 Expectation and Variance -- 2.5.3.1 Expectation -- 2.5.3.2 Population Variance -- 2.5.4 Examples of Distribution Functions -- 2.5.4.1 The Gaussian (Normal) Random Variable and Distribution -- 2.5.4.2 Bernoulli Random Variable -- 2.5.4.3 Uniform Random Variable -- 2.5.4.4 A Poisson Random Variable -- 2.5.4.5 The Lognormal Distribution -- 2.5.5 The Empirical Distribution Function versus the Distribution Model -- 2.5.6 Constructing a Distribution Function from Data -- 2.5.7 Monte Carlo Simulation -- 2.5.8 Data Transformations -- 2.6 Bivariate Data Analysis -- 2.6.1 Introduction -- 2.6.2 Graphical Methods: Scatter plots -- 2.6.3 Data Summary: Correlation (Coefficient) -- 2.6.3.1 Definition -- 2.6.3.2 Properties of r -- Further Reading -- 3 Modeling Uncertainty: Concepts and Philosophies -- 3.1 What is Uncertainty? -- 3.2 Sources of Uncertainty -- 3.3 Deterministic Modeling -- 3.4 Models of Uncertainty -- 3.5 Model and Data Relationship. 327 $a3.6 Bayesian View on Uncertainty -- 3.7 Model Verification and Falsification -- 3.8 Model Complexity -- 3.9 Talking about Uncertainty -- 3.10 Examples -- 3.10.1 Climate Modeling -- 3.10.1.1 Description -- 3.10.1.2 Creating Data Sets Using Models -- 3.10.1.3 Parameterization of Subgrid Variability -- 3.10.1.4 Model Complexity -- 3.10.2 Reservoir Modeling -- 3.10.2.1 Description -- 3.10.2.2 Creating Data Sets Using Models -- 3.10.2.3 Parameterization of Subgrid Variability -- 3.10.2.4 Model Complexity -- Further Reading -- 4 Engineering the Earth: Making Decisions Under Uncertainty -- 4.1 Introduction -- 4.2 Making Decisions -- 4.2.1 Example Problem -- 4.2.2 The Language of Decision Making -- 4.2.3 Structuring the Decision -- 4.2.4 Modeling the Decision -- 4.2.4.1 Payoffs and Value Functions -- 4.2.4.2 Weighting -- 4.2.4.3 Trade-Offs -- 4.2.4.4 Sensitivity Analysis -- 4.3 Tools for Structuring Decision Problems -- 4.3.1 Decision Trees -- 4.3.2 Building Decision Trees -- 4.3.3 Solving Decision Trees -- 4.3.4 Sensitivity Analysis -- Further Reading -- 5 Modeling Spatial Continuity -- 5.1 Introduction -- 5.2 The Variogram -- 5.2.1 Autocorrelation in 1D -- 5.2.2 Autocorrelation in 2D and 3D -- 5.2.3 The Variogram and Covariance Function -- 5.2.4 Variogram Analysis -- 5.2.4.1 Anisotropy -- 5.2.4.2 What is the Practical Meaning of a Variogram? -- 5.2.5 A Word on Variogram Modeling -- 5.3 The Boolean or Object Model -- 5.3.1 Motivation -- 5.3.2 Object Models -- 5.4 3D Training Image Models -- Further Reading -- 6 Modeling Spatial Uncertainty -- 6.1 Introduction -- 6.2 Object-Based Simulation -- 6.3 Training Image Methods -- 6.3.1 Principle of Sequential Simulation -- 6.3.2 Sequential Simulation Based on Training Images -- 6.3.3 Example of a 3D Earth Model -- 6.4 Variogram-Based Methods -- 6.4.1 Introduction -- 6.4.2 Linear Estimation. 327 $a6.4.3 Inverse Square Distance -- 6.4.4 Ordinary Kriging -- 6.4.5 The Kriging Variance -- 6.4.6 Sequential Gaussian Simulation -- 6.4.6.1 Kriging to Create a Model of Uncertainty -- 6.4.6.2 Using Kriging to Perform (Sequential) Gaussian Simulation -- Further Reading -- 7 Constraining Spatial Models of Uncertainty with Data -- 7.1 Data Integration -- 7.2 Probability-Based Approaches -- 7.2.1 Introduction -- 7.2.2 Calibration of Information Content -- 7.2.3 Integrating Information Content -- 7.2.4 Application to Modeling Spatial Uncertainty -- 7.3 Variogram-Based Approaches -- 7.4 Inverse Modeling Approaches -- 7.4.1 Introduction -- 7.4.2 The Role of Bayes' Rule in Inverse Model Solutions -- 7.4.3 Sampling Methods -- 7.4.3.1 Rejection Sampling -- 7.4.3.2 Metropolis Sampler -- 7.4.4 Optimization Methods -- Further Reading -- 8 Modeling Structural Uncertainty -- 8.1 Introduction -- 8.2 Data for Structural Modeling in the Subsurface -- 8.3 Modeling a Geological Surface -- 8.4 Constructing a Structural Model -- 8.4.1 Geological Constraints and Consistency -- 8.4.2 Building the Structural Model -- 8.5 Gridding the Structural Model -- 8.5.1 Stratigraphic Grids -- 8.5.2 Grid Resolution -- 8.6 Modeling Surfaces through Thicknesses -- 8.7 Modeling Structural Uncertainty -- 8.7.1 Sources of Uncertainty -- 8.7.2 Models of Structural Uncertainty -- Further Reading -- 9 Visualizing Uncertainty -- 9.1 Introduction -- 9.2 The Concept of Distance -- 9.3 Visualizing Uncertainty -- 9.3.1 Distances, Metric Space and Multidimensional Scaling -- 9.3.2 Determining the Dimension of Projection -- 9.3.3 Kernels and Feature Space -- 9.3.4 Visualizing the Data-Model Relationship -- Further Reading -- 10 Modeling Response Uncertainty -- 10.1 Introduction -- 10.2 Surrogate Models and Ranking -- 10.3 Experimental Design and Response Surface Analysis -- 10.3.1 Introduction. 327 $a10.3.2 The Design of Experiments -- 10.3.3 Response Surface Designs -- 10.3.4 Simple Illustrative Example -- 10.3.5 Limitations -- 10.4 Distance Methods for Modeling Response Uncertainty -- 10.4.1 Introduction -- 10.4.2 Earth Model Selection by Clustering -- 10.4.2.1 Introduction -- 10.4.2.2 k-Means Clustering -- 10.4.2.3 Clustering of Earth Models for Response Uncertainty Evaluation -- 10.4.3 Oil Reservoir Case Study -- 10.4.4 Sensitivity Analysis -- 10.4.5 Limitations -- Further Reading -- 11 Value of Information -- 11.1 Introduction -- 11.2 The Value of Information Problem -- 11.2.1 Introduction -- 11.2.2 Reliability versus Information Content -- 11.2.3 Summary of the VOI Methodology -- 11.2.3.1 Steps 1 and 2: VOI Decision Tree -- 11.2.3.2 Steps 3 and 4: Value of Perfect Information -- 11.2.3.3 Step 5: Value of Imperfect Information -- 11.2.4 Value of Information for Earth Modeling Problems -- 11.2.5 Earth Models -- 11.2.6 Value of Information Calculation -- 11.2.7 Example Case Study -- 11.2.7.1 Introduction -- 11.2.7.2 Earth Modeling -- 11.2.7.3 Decision Problem -- 11.2.7.4 The Possible Data Sources -- 11.2.7.5 Data Interpretation -- Further Reading -- 12 Example Case Study -- 12.1 Introduction -- 12.1.1 General Description -- 12.1.2 Contaminant Transport -- 12.1.3 Costs Involved -- 12.2 Solution -- 12.2.1 Solving the Decision Problem -- 12.2.2 Buying More Data -- 12.2.2.1 Buying Geological Information -- 12.2.2.2 Buying Geophysical Information -- 12.3 Sensitivity Analysis -- Index. 330 $a'Modeling Uncertainty in the Earth Sciences' highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex earth systems and the impact that it has on practical situations. 330 $bModeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding of the best way to make decisions under uncertainty for Earth Science problems. The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of 'engineering' the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides an invaluable primer for the complex area of decision making with uncertainty in the Earth Sciences. Modeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding on how to make optimal decisions under uncertainty for Earth Science problems. The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of 'engineering' the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides and invaluable primer for the complex areas of decision making with uncertainty in the Earth Sciences. First comprehensive book to present the uncertainties inherent in modeling for the Earth science community Full colour throughout Includes case study examples for greater clarity Geo-engineering focus Provides an accessible introduction to modeling uncertainty in the Earth Sciences Includes established tools as well as novel techniques developed by the author Companion website available with free software and examples Avoids complex mathematics for enhanced user-friendly approach Companion Website www.wiley.com/go/caers/modeling 606 $aGeology$xMathematical models 606 $aEarth sciences$xStatistical methods 606 $aThree-dimensional imaging in geology 606 $aUncertainty 615 0$aGeology$xMathematical models. 615 0$aEarth sciences$xStatistical methods. 615 0$aThree-dimensional imaging in geology. 615 0$aUncertainty. 676 $a550.15118 700 $aCaers$b Jef$0732927 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141250103321 996 $aModeling uncertainty in the earth sciences$91985742 997 $aUNINA