05514nam 2200721Ia 450 991100661780332120200520144314.097866121691069781282169104128216910697800809317600080931766(CKB)1000000000748356(EBL)428726(OCoLC)424572012(SSID)ssj0000162093(PQKBManifestationID)11159381(PQKBTitleCode)TC0000162093(PQKBWorkID)10200658(PQKB)10580044(MiAaPQ)EBC428726(PPN)170246035(FR-PaCSA)41001470(FRCYB41001470)41001470(EXLCZ)99100000000074835620090401d2009 uy| 0engur|n|---|||||txtccrGeophysical electromagnetic theory and methods /Michael S. ZhdanovOxford Elsevier20091 online resource (869 p.)Methods in geochemistry and geophysics ;43Description based upon print version of record.9780444638915 0444638911 9780444529633 0444529632 Includes bibliographical references and index.Front Cover; Geophysical Electromagnetic Theory and Methods; Copyright Page; Contents; Preface; Part I: Introduction to Field Theory; Chapter 1. Differential Calculus of Vector Fields and Differential Forms; 1.1 The Basic Differential Relationships of Field Theory; 1.2 The Basic Integral Relationships of Field Theory; 1.3 Differential Forms in Field Theory; References and Recommended Reading; Chapter 2. Foundations of Field Theory; 2.1 Field Generation; 2.2 Stationary Field Equations and Methods of Their Solutions; 2.3 Scalar and Vector Potentials of the Stationary Field2.4 Nonstationary Fields and Differential FormsReferences and Recommended Reading; Part II: Foundations of Electromagnetic Theory; Chapter 3. Electromagnetic Field Equations; 3.1 Maxwell's Equations and Boundary Conditions; 3.2 Time-Harmonic Electromagnetic Field; 3.3 Electromagnetic Energy and Poynting's Theorem; 3.4 Electromagnetic Green's Tensors; 3.5 Reciprocity Relations; References and Recommended Reading; Chapter 4. Models of Electromagnetic Induction in the Earth; 4.1 Models of Electromagnetic Fields; 4.2 Static Electromagnetic Fields4.3 Electromagnetic Field Diffusion in Conductive Media4.4 Electromagnetic Waves; References and Recommended Reading; Chapter 5. Electromagnetic Fields in Horizontally Stratified Media; 5.1 Plane Wave Propagation in a Layered Earth; 5.2 Spectral Method of Computing EM Fields in Horizontally Stratified Media; 5.3 Electromagnetic Field of an Arbitrary System of Magnetospheric Currents in a Horizontally Homogeneous Medium; 5.4 Eectromagnetic Fields Generated in Layered Earth by Electric and Magnetic Dipole Transmitters; References and Recommended ReadingChapter 6. Electromagnetic Fields in Inhomogeneous Media6.1 Integral Equation Method; 6.2 Integral Equation Method in Models with Inhomogeneous Background Conductivity; 6.3 Family of Linear and Nonlinear Integral Approximations of the Electromagnetic Field; 6.4 Differential Equation Methods; References and Recommended Reading; Part III: Inversion and Imaging of Electromagnetic Field Data; Chapter 7. Principles of Ill-Posed Inverse Problem Solution; 7.1 Ill-Posed Inverse Problems; 7.2 Foundations of Regularization Theory; 7.3 Regularization Parameter; References and Recommended ReadingChapter 8. Electromagnetic Inversion8.1 Linear Inversions; 8.2 Nonlinear Inversion; 8.3 Quasi-Linear Inversion; 8.4 Quasi-Analytical Inversion; References and Recommended Reading; Chapter 9. Electromagnetic Migration; 9.1 Electromagnetic Migration in the Time Domain; 9.2 Analytic Continuation and Migration in the (k,ω) Domain; 9.3 Finite Difference Migration; 9.4 Visualization of Geoelectric Structures by Use of Migration in the Frequency and Time Domains; 9.5 Migration Versus Inversion; References and Recommended Reading; Part IV: Geophysical Electromagnetic MethodsChapter 10. Electromagnetic Properties of Rocks and MineralsIn this book the author presents state-of-the-art geophysical electromagnetic (EM) theory and methods of EM geophysics. The book brings together fundamental theory of EM field and practical aspects of EM exploration for mineral and energy resources. The book is divided in four parts covering the foundations of the field theory and its applications to the applied electromagnetic geophysics, including new emerging methods of the marine EM exploration. The first part is an introduction to the field theory required for understanding the basics of geophysical electromagnetic theory. The second Methods in geochemistry and geophysics ;43.Electromagnetic measurementsProspectingGeophysical methodsElectromagnetic measurements.ProspectingGeophysical methods.622.153622.15Zhdanov Michael S53600MiAaPQMiAaPQMiAaPQBOOK9911006617803321Geophysical electromagnetic theory and methods4470263UNINA03910nam 2200961z- 450 991055766080332120210501(CKB)5400000000044898(oapen)https://directory.doabooks.org/handle/20.500.12854/68741(oapen)doab68741(oapen)68741(EXLCZ)99540000000004489820202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning in InsuranceBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (260 p.)3-03936-447-2 3-03936-448-0 Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries' "preferred methods" were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.History of engineering and technologybicsscaccelerated failure time modelanalyzing financial dataautocorrelationautomobile insurancebenchmarkBornhuetter-Fergusoncalibrationcanonical parameterschain ladderchain-ladder methodclaims predictioncross-validationdeposit insurancedichotomous responseexponential familiesexport credit insurancegeneralised linear modellingGLMimplied volatilityleast-squares monte carlo methodlife insurancelocal linear kernel estimationlong-term forecastsmachine learningmaximum likelihoodn/anon-life reservingoperational timeoverdispersionoverlapping returnsparameterizationpredictionpredictive modelprior knowledgeproxy modelingrisk classificationrisk selectionsemiparametric modelingSolvency IIstatic arbitragestock return volatilitytelematicstree boostingvalidationVaR estimationzero-inflated poisson modelzero-inflationHistory of engineering and technologyNielsen Jens Perchedt1314788Asimit AlexandruedtKyriakou IoannisedtNielsen Jens PerchothAsimit AlexandruothKyriakou IoannisothBOOK9910557660803321Machine Learning in Insurance3031967UNINA