LEADER 03548nam 2200409 450 001 9910480555303321 005 20210803144403.0 010 $a1-5275-1856-6 035 $a(CKB)4100000007102374 035 $a(MiAaPQ)EBC5568583 035 $a(Au-PeEL)EBL5568583 035 $a(OCoLC)1060198888 035 $a(EXLCZ)994100000007102374 100 $a20181123d2018 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aIndia as a model for global development /$fedited by Mahmoud Masaeli and Monica Prabhakar 210 1$aNewcastle upon Tyne, England :$cCambridge Scholars Publishing,$d2018. 215 $a1 online resource (xiii, 202 pages) 311 $a1-5275-1658-X 327 $aIntro -- Table of Contents -- Notes for the Readers -- Part I -- Chapter One -- Chapter Two -- Chapter Three -- Chapter Four -- Chapter Five -- Part II -- Chapter Six -- Chapter Seven -- Chapter Eight -- Chapter Nine -- Chapter Ten -- Chapter Eleven -- Chapter Twelve. 330 $aIndia is an emerging market economy, and has been more successful than most other emerging economies. Key to this success are India's ancient legacy of consensus democracy, non-violence, multi-culturality, tolerance, secularism, and the practical simplicity of economic life inspired by Mahatma Gandhi. Also, vital to India's present economy is the history of the country since the struggle for Independence began in 1857. India has followed a strikingly distinct route of development from other emerging economies such as South Korea, China, Malaysia, Brazil, and Mexico. While these countries concentrated on manufacturing and exports, India grounded its economy on an integrative domestic system of life. This model is marked by interesting and gradual, but constant, growth with an emphasis on services.Reforms in land-agricultural system, political governance, and financial management have led to a landmark stage of economic progress, with India's GDP rate higher than many emerging market economies. This volume explores the reasons why India has fared better than other emerging market economies, and whether other countries can take inspiration from this model and rebuild their own countries based on their national resources, cultural heritage, and the capacity to interact globally.The book is inspired by Mahatma Gandhi's 'India of my Dreams'. It would be entirely unrealistic to claim that India's development model is all positive or meets the standards of India of Gandhi's dreams. Gandhi was a great proponent of the self-sufficiency of villages and of the bourgeoning of cottage industries. However, in present day India, debt-ridden farmers' suicide rates are drastic and the crafts are dying. In finding answers to why this is so, the volume looks at the failures in the development of cottage industries, whether the efforts of NGOs in this regard are 330 8 $asufficient, and whether Amartya Sen's capabilities approach would complement Gandhi's 'self-sufficiency of villages' perspective in order to preserve crafts and indigenous production systems while continuing with industrialization and agrarian reforms. 608 $aElectronic books. 676 $a338.954 702 $aMasaeli$b Mahmoud 702 $aPrabhakar$b Monica 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910480555303321 996 $aIndia as a model for global development$92453812 997 $aUNINA LEADER 06189nam 2200697 a 450 001 9910139578003321 005 20200520144314.0 010 $a1-119-96140-8 010 $a1-283-28311-5 010 $a9786613283115 010 $a1-118-30535-3 010 $a1-119-95295-6 010 $a1-119-95296-4 035 $a(CKB)2550000000052719 035 $a(EBL)819173 035 $a(OCoLC)763160180 035 $a(SSID)ssj0000541559 035 $a(PQKBManifestationID)11351597 035 $a(PQKBTitleCode)TC0000541559 035 $a(PQKBWorkID)10499089 035 $a(PQKB)11435366 035 $a(MiAaPQ)EBC819173 035 $a(Au-PeEL)EBL819173 035 $a(CaPaEBR)ebr10500952 035 $a(CaONFJC)MIL328311 035 $a(PPN)243652593 035 $a(EXLCZ)992550000000052719 100 $a20110715d2011 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical pattern recognition$b[electronic resource] /$fAndrew R. Webb, Keith D. Copsey 205 $a3rd ed. 210 $aHoboken $cWiley$d2011 215 $a1 online resource (xxiv, 642 pages) $cillustrations, tables 300 $aDescription based upon print version of record. 311 $a0-470-68227-2 311 $a0-470-68228-0 320 $aIncludes bibliographical references and index. 327 $aStatistical Pattern Recognition; Contents; Preface; Notation; 1 Introduction to Statistical Pattern Recognition; 1.1 Statistical Pattern Recognition; 1.1.1 Introduction; 1.1.2 The Basic Model; 1.2 Stages in a Pattern Recognition Problem; 1.3 Issues; 1.4 Approaches to Statistical Pattern Recognition; 1.5 Elementary Decision Theory; 1.5.1 Bayes' Decision Rule for Minimum Error; 1.5.2 Bayes' Decision Rule for Minimum Error - Reject Option; 1.5.3 Bayes' Decision Rule for Minimum Risk; 1.5.4 Bayes' Decision Rule for Minimum Risk - Reject Option; 1.5.5 Neyman-Pearson Decision Rule 327 $a1.5.6 Minimax Criterion1.5.7 Discussion; 1.6 Discriminant Functions; 1.6.1 Introduction; 1.6.2 Linear Discriminant Functions; 1.6.3 Piecewise Linear Discriminant Functions; 1.6.4 Generalised Linear Discriminant Function; 1.6.5 Summary; 1.7 Multiple Regression; 1.8 Outline of Book; 1.9 Notes and References; Exercises; 2 Density Estimation - Parametric; 2.1 Introduction; 2.2 Estimating the Parameters of the Distributions; 2.2.1 Estimative Approach; 2.2.2 Predictive Approach; 2.3 The Gaussian Classifier; 2.3.1 Specification; 2.3.2 Derivation of the Gaussian Classifier Plug-In Estimates 327 $a2.3.3 Example Application Study2.4 Dealing with Singularities in the Gaussian Classifier; 2.4.1 Introduction; 2.4.2 Na ??ve Bayes; 2.4.3 Projection onto a Subspace; 2.4.4 Linear Discriminant Function; 2.4.5 Regularised Discriminant Analysis; 2.4.6 Example Application Study; 2.4.7 Further Developments; 2.4.8 Summary; 2.5 Finite Mixture Models; 2.5.1 Introduction; 2.5.2 Mixture Models for Discrimination; 2.5.3 Parameter Estimation for Normal Mixture Models; 2.5.4 Normal Mixture Model Covariance Matrix Constraints; 2.5.5 How Many Components?; 2.5.6 Maximum Likelihood Estimation via EM 327 $a2.5.7 Example Application Study2.5.8 Further Developments; 2.5.9 Summary; 2.6 Application Studies; 2.7 Summary and Discussion; 2.8 Recommendations; 2.9 Notes and References; Exercises; 3 Density Estimation - Bayesian; 3.1 Introduction; 3.1.1 Basics; 3.1.2 Recursive Calculation; 3.1.3 Proportionality; 3.2 Analytic Solutions; 3.2.1 Conjugate Priors; 3.2.2 Estimating the Mean of a Normal Distribution with Known Variance; 3.2.3 Estimating the Mean and the Covariance Matrix of a Multivariate Normal Distribution; 3.2.4 Unknown Prior Class Probabilities; 3.2.5 Summary; 3.3 Bayesian Sampling Schemes 327 $a3.3.1 Introduction3.3.2 Summarisation; 3.3.3 Sampling Version of the Bayesian Classifier; 3.3.4 Rejection Sampling; 3.3.5 Ratio of Uniforms; 3.3.6 Importance Sampling; 3.4 Markov Chain Monte Carlo Methods; 3.4.1 Introduction; 3.4.2 The Gibbs Sampler; 3.4.3 Metropolis-Hastings Algorithm; 3.4.4 Data Augmentation; 3.4.5 Reversible Jump Markov Chain Monte Carlo; 3.4.6 Slice Sampling; 3.4.7 MCMC Example - Estimation of Noisy Sinusoids; 3.4.8 Summary; 3.4.9 Notes and References; 3.5 Bayesian Approaches to Discrimination; 3.5.1 Labelled Training Data; 3.5.2 Unlabelled Training Data 327 $a3.6 Sequential Monte Carlo Samplers 330 $a"Statistical Pattern Recognition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book describes techniques for analysing data comprising measurements made on individuals or objects. The techniques are used to make a prediction such as disease of a patient, the type of object illuminated by a radar, economic forecast. Emphasis is placed on techniques for classification, a term used for predicting the class or group an object belongs to (based on a set of exemplars) and for methods that seek to discover natural groupings in a data set. Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques and includes a variety of exercises, from 'open-book' questions to more lengthy projects. New material is presented, including the analysis of complex networks and basic techniques for analysing the properties of datasets and also introduces readers to the use of variational methods for Bayesian density estimation and looks at new applications in biometrics and security"--$cProvided by publisher. 606 $aPattern perception$xStatistical methods 615 0$aPattern perception$xStatistical methods. 676 $a006.4 686 $aMAT029000$2bisacsh 700 $aWebb$b A. R$g(Andrew R.)$0915397 701 $aCopsey$b Keith D$0915398 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139578003321 996 $aStatistical pattern recognition$92051917 997 $aUNINA LEADER 01348ncm 2200385Ia 450 001 996386917903316 005 20221108092710.0 035 $a(CKB)1000000000616510 035 $a(EEBO)2264215011 035 $a(OCoLC)09012034 035 $a(EXLCZ)991000000000616510 100 $a19821202d1663 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 00$aCatch that catch can, or, A new collection of catches, rounds, and canons$b[electronic resource] $ebeing three or four parts in one 210 $aLondon $cPrinted by W.G. for John Playford and Zachariah Watkins$d1663 215 $a[7], 128, 31, [1] p. of music 300 $a"To all lovers of musick," signed: John Hilton. 300 $a"Canons or hymnes of three and foure parts in one": p. [1]-31 (3rd grouping) 300 $aReproduction of original in the British Library. 330 $aeebo-0018 606 $aGlees, catches, rounds, etc 606 $aCanons, fugues, etc 615 0$aGlees, catches, rounds, etc. 615 0$aCanons, fugues, etc. 701 $aHilton$b John$f1599-1657.$01005363 801 0$bUMI 801 1$bUMI 801 2$bOCL 801 2$bUMI 801 2$bWaOLN 906 $aBOOK 912 $a996386917903316 996 $aCatch that catch can, or, A new collection of catches, rounds, and canons$92331272 997 $aUNISA LEADER 02031nam 2200481 450 001 9910707311403321 005 20160802153213.0 035 $a(CKB)5470000002464408 035 $a(OCoLC)954495957 035 $a(EXLCZ)995470000002464408 100 $a20160802j199611 ua 0 101 0 $aeng 135 $aurbn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA computational-experimental development of vortex generator use for a transitioning S-diffuser /$fBruce J. Wendt and Julianne C. Dudek 210 1$aCleveland, Ohio :$cNational Aeronautics and Space Administration, Lewis Research Center,$dNovember 1996. 215 $a1 online resource (9 pages) $cillustrations 225 1 $aNASA technical memorandum ;$v107357 300 $aTitle from title screen (viewed Aug. 2, 2016). 300 $a"November 1996"--Report documentation page. 300 $a"Prepared for the 1996 International Mechanical Engineering Congress and Exhibit sponsored by the American Society of Mechanical Engineers, Atlanta, Georgia, November 17-22, 1996." 300 $a"Performing organization: National Aeronautics and Space Administration, Lewis Research Center"--Report documentation page. 320 $aIncludes bibliographical references (pages 8-9). 606 $aVortex generators$2nasat 606 $aDiffusers$2nasat 606 $aNavier-Stokes equation$2nasat 606 $aPressure recovery$2nasat 615 7$aVortex generators. 615 7$aDiffusers. 615 7$aNavier-Stokes equation. 615 7$aPressure recovery. 700 $aWendt$b Bruce J.$01412377 702 $aDudek$b Julianne C. 712 02$aLewis Research Center, 712 02$aUnited States.$bNational Aeronautics and Space Administration, 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910707311403321 996 $aA computational-experimental development of vortex generator use for a transitioning S-diffuser$93505737 997 $aUNINA