05479nam 2200685 a 450 991014371470332120170809164705.01-280-85495-297866108549500-470-06566-40-470-06567-2(CKB)1000000000357364(EBL)292573(OCoLC)476052538(SSID)ssj0000109289(PQKBManifestationID)11145290(PQKBTitleCode)TC0000109289(PQKBWorkID)10047352(PQKB)10686582(MiAaPQ)EBC292573(EXLCZ)99100000000035736420061010d2007 uy 0engur|n|---|||||txtccrBayes linear statistics[electronic resource] theory and methods /Michael Goldstein and David WooffChichester, England ;Hoboken, NJ John Wileyc20071 online resource (538 p.)Wiley series in probability and statisticsDescription based upon print version of record.0-470-01562-4 Includes bibliographical references (p. [497]-502) and index.Bayes Linear Statistics; Contents; Preface; 1 The Bayes linear approach; 1.1 Combining beliefs with data; 1.2 The Bayesian approach; 1.3 Features of the Bayes linear approach; 1.4 Example; 1.4.1 Expectation, variance, and standardization; 1.4.2 Prior inputs; 1.4.3 Adjusted expectations; 1.4.4 Adjusted versions; 1.4.5 Adjusted variances; 1.4.6 Checking data inputs; 1.4.7 Observed adjusted expectations; 1.4.8 Diagnostics for adjusted beliefs; 1.4.9 Further diagnostics for the adjusted versions; 1.4.10 Summary of basic adjustment; 1.4.11 Diagnostics for collections1.4.12 Exploring collections of beliefs via canonical structure1.4.13 Modifying the original specifications; 1.4.14 Repeating the analysis for the revised model; 1.4.15 Global analysis of collections of observations; 1.4.16 Partial adjustments; 1.4.17 Partial diagnostics; 1.4.18 Summary; 1.5 Overview; 2 Expectation; 2.1 Expectation as a primitive; 2.2 Discussion: expectation as a primitive; 2.3 Quantifying collections of uncertainties; 2.4 Specifying prior beliefs; 2.4.1 Example: oral glucose tolerance test; 2.5 Qualitative and quantitative prior specification2.6 Example: qualitative representation of uncertainty2.6.1 Identifying the quantities of interest; 2.6.2 Identifying relevant prior information; 2.6.3 Sources of variation; 2.6.4 Representing population variation; 2.6.5 The qualitative representation; 2.6.6 Graphical models; 2.7 Example: quantifying uncertainty; 2.7.1 Prior expectations; 2.7.2 Prior variances; 2.7.3 Prior covariances; 2.7.4 Summary of belief specifications; 2.8 Discussion: on the various methods for assigning expectations; 3 Adjusting beliefs; 3.1 Adjusted expectation; 3.2 Properties of adjusted expectation3.3 Adjusted variance3.4 Interpretations of belief adjustment; 3.5 Foundational issues concerning belief adjustment; 3.6 Example: one-dimensional problem; 3.7 Collections of adjusted beliefs; 3.8 Examples; 3.8.1 Algebraic example; 3.8.2 Oral glucose tolerance test; 3.8.3 Many oral glucose tolerance tests; 3.9 Canonical analysis for a belief adjustment; 3.9.1 Canonical directions for the adjustment; 3.9.2 The resolution transform; 3.9.3 Partitioning the resolution; 3.9.4 The reverse adjustment; 3.9.5 Minimal linear sufficiency; 3.9.6 The adjusted belief transform matrix3.10 The geometric interpretation of belief adjustment3.11 Examples; 3.11.1 Simple one-dimensional problem; 3.11.2 Algebraic example; 3.11.3 Oral glucose tolerance test; 3.12 Further reading; 4 The observed adjustment; 4.1 Discrepancy; 4.1.1 Discrepancy for a collection; 4.1.2 Evaluating discrepancy over a basis; 4.1.3 Discrepancy for quantities with variance zero; 4.2 Properties of discrepancy measures; 4.2.1 Evaluating the discrepancy vector over a basis; 4.3 Examples; 4.3.1 Simple one-dimensional problem; 4.3.2 Detecting degeneracy; 4.3.3 Oral glucose tolerance test4.4 The observed adjustmentBayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian methodology in that it establishes simpler approaches to belief specification and analysis based around expectation judgements. Bayes Linear Statistics presents an authoritative account of this approach, explaining the foundations, theory, methodolWiley series in probability and statistics.Bayesian statistical decision theoryLinear systemsComputational complexityElectronic books.Bayesian statistical decision theory.Linear systems.Computational complexity.519.5519.542Goldstein Michael1949-924221Wooff David311186MiAaPQMiAaPQMiAaPQBOOK9910143714703321Bayes linear statistics2074085UNINA01766nam 2200409 a 450 991069800710332120090303160119.0(CKB)5470000002394936(OCoLC)311867952(EXLCZ)99547000000239493620090303d1985 ua 0engurmn||||a||||txtrdacontentcrdamediacrrdacarrierHabitat suitability index models and instream flow suitability curvesAmerican shad[electronic resource] /by David J. Stier and Johnie H. Crance ; performed for National Coastal Ecosystems Team, Division of Biological Services, Research and Development, Fish and Wildlife Service, U.S. Department of the InteriorWashington, DC :National Coastal Ecosystems Team, Division of Biological Services, Research and Development, Fish and Wildlife Service, U.S. Dept. of the Interior,[1985]vi, 34 pages illustrations, 1 form ;28 cmBiological report ;82(10.88)Title from title screen (viewed on Sept. 11, 2008)."June 1985."Includes bibliographical referencse (pages 27-34).American shadAmerican shadHabitat partitioning (Ecology)Mathematical modelsAmerican shad.Habitat partitioning (Ecology)Mathematical models.Stier David J1420708Crance Johnie H1400170National Coastal Ecosystems Team (U.S.)GPOGPOBOOK9910698007103321Habitat suitability index models and instream flow suitability curves3539527UNINA04219nam 22005775 450 991079658320332120230125200051.01-78684-901-11-4648-1047-810.1596/978-1-4648-1046-6(CKB)3840000000332965(MiAaPQ)EBC5301730(CaBNVSL)gtp00567777(Credo)wbchanging2018(OCoLC)1054296745(Credo)9781786849014(The World Bank)211046(US-djbf)211046(EXLCZ)99384000000033296520020129d2009 uf 0engurcn|||||||||txtrdacontentcrdamediacrrdacarrierThe Changing Wealth of Nations 2018 : Building a Sustainable Future /Glenn-Marie Lange[Enhanced Credo edition]Washington, D.C. :The World Bank,2018.1 online resource (240 pages)1-4648-1046-X Includes bibliographical references.Executive summary -- Estimating the wealth of nations -- Richer or poorer? Global and regional trends in wealth from 1995 to 2014 -- Wealth accounts, adjusted net saving, and diversified development in resource-rich African countries -- Expanding measures of productivity to include natural capital -- The carbon wealth of nations: from rents to risks -- Human capital and the wealth of nations: global estimates and trends -- Gains in human capital wealth: what growth models tell us -- Intangible capital as the engine for development in Morocco -- Air pollution: impact on human health and wealth -- Subsidies reduce marine fisheries wealth -- Remote sensing and modeling to fill the gap in "missing" natural capital -- Appendix A: summary of methodology and data sources -- Appendix B: per capital wealth for 2014.Countries regularly track gross domestic product (GDP) as an indicator of their economic progress, but not wealth-the assets such as infrastructure, forests, minerals, and human capital that produce GDP. In contrast, corporations routinely report on both their income and assets to assess their economic health and prospects for the future. Wealth accounts allow countries to take stock of their assets to monitor the sustainability of development, an urgent concern today for all countries. The Changing Wealth of Nations 2018: Building a Sustainable Future covers national wealth for 141 countries over 20 years (1995-2014) as the sum of produced capital, 19 types of natural capital, net foreign assets, and human capital overall as well as by gender and type of employment. Great progress has been made in estimating wealth since the first volume, Where Is the Wealth of Nations? Measuring Capital for the 21st Century, was published in 2006. New data substantially improve estimates of natural capital, and, for the first time, human capital is measured by using household surveys to estimate lifetime earnings. The Changing Wealth of Nations 2018 begins with a review of global and regional trends in wealth over the past two decades and provides examples of how wealth accounts can be used for the analysis of development patterns. Several chapters discuss the new work on human capital and its application in development policy. The book then tackles elements of natural capital that are not yet fully incorporated in the wealth accounts: air pollution, marine fisheries, and ecosystems. This book targets policy makers but will engage anyone committed to building a sustainable future for the planet.World Bank e-Library.Economic indicatorsSustainable developmentEconomic indicators.Sustainable development.330.9Lange Glenn-Marie140541Lange Glenn-MarieWodon QuentinCarey Kevin1967-World Bank,Credo Reference (Firm),DJBFDJBFBOOK9910796583203321The Changing Wealth of Nations 20183814068UNINA01240nam1 22002653i 450 VAN0008150820240806100623.94820110120f |0itac50 baitaIT|||| |||||Manuale di storia del diritto italianoFederico CiccaglioneMilanoVallardivol.25 cm.001VAN000124052001 Biblioteca giuridica contemporanea210 MilanoVallardi.001VAN000815112001 ˆ<<‰Manuale di storia del diritto italiano>> 1Federico Ciccaglione205 Milano : Vallardi[1901]210 XII482 p. ; 25 cm215 Fondo Salvatore Biggiero.1001VAN000815122001 ˆ<<‰Manuale di storia del diritto italiano>> 2Federico Ciccaglione205 Milano : Vallardi[1901?]210 VIII529 p. ; 25 cm215 Fondo Salvatore Biggiero.2DirittoItaliaStoriaSec. 19.-20.VANC012073FIMilanoVANL000284CiccaglioneFedericoVANV066766176980Vallardi <editore>VANV110668650ITSOL20240906RICAVAN00081508Manuale di storia del diritto italiano732595UNICAMPANIA