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[null,"1.","Record Nr.",null,"UNISA996391811603316"]
[null,null,"Autore",null,"Isaacson Henry <1581-1654.>"]
[null,null,"Titolo",null,"A treaty of pacification [[electronic resource] ] : Or Conditions of peace between God and man. / / By H.I"]
[null,null,"Pubbl/distr/stampa",null,"1642. London, : Printed for R. Thral, [1642]"]
[null,null,"Descrizione fisica",null,"[14], 91, [1] p"]
[null,null,"Soggetti",null,"Christian life"]
[{"$nulls":4},"God - Knowableness"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","H.I. = Henry Isaacson."]
[{"$nulls":4},"The title page is engraved and signed: W.M. sculp:, i.e. William Marshall."]
[{"$nulls":4},"Running title reads: A treaty of pacification between God and man."]
[{"$nulls":4},"Reproduction of the original in the British Library."]
[null,null,null,"Sommario/riassunto","eebo-0018"]
[null,"2.","Record Nr.",null,"UNINA9910300151603321"]
[null,null,"Autore",null,"Abbas Casim"]
[null,null,"Titolo",null,"An introduction to compactness results in symplectic field theory / / Casim Abbas"]
[null,null,"Pubbl/distr/stampa",null,"Heidelberg, Germany : , : Springer, , 2014"]
[null,null,"ISBN",null,"3-642-31543-7"]
[null,null,"Edizione",null,"[1st ed. 2014.]"]
[null,null,"Descrizione fisica",null,"1 online resource (viii, 252 pages) : illustrations (some color)"]
[null,null,"Collana",null,"Gale eBooks"]
[null,null,"Classificazione",null,"SK 370"]
[null,null,"Disciplina",null,"510"]
[{"$nulls":4},"514.34"]
[{"$nulls":4},"516.3"]
[{"$nulls":4},"516.3/6"]
[null,null,"Soggetti",null,"Symplectic geometry"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"\"\"An Introduction to Compactness Results in Symplectic Field Theory\"\"; \"\"Preface\"\"; \"\"Contents\"\"; \"\"Chapter 1: Riemann Surfaces\"\"; \"\"1.1 Smooth and Noded Riemann Surfaces\"\"; \"\"1.2 Riemann Surfaces and Hyperbolic Geometry\"\"; \"\"1.2.1 Stable Surfaces\"\"; \"\"1.2.2 The Hyperbolic Plane\"\"; \"\"1.2.3 Gluing Hyperbolic Surfaces Along Their Boundaries\"\"; \"\"1.2.4 Annuli\"\"; \"\"1.2.5 Hexagons in the Upper Half Plane and Pairs of Pants\"\"; \"\"1.2.6 Pairs of Pants Decompositions\"\"; \"\"1.2.7 Thick-Thin Decomposition and Collar Lemma\"\"; \"\"1.3 The Deligne-Mumford Compactness Result\"\""]
[{"$nulls":4},"\"\"1.3.1 The Notion of Convergence\"\"\"\"1.3.2 The Proof of the Compactness Result for Surfaces Without Boundary\"\"; \"\"1.3.3 Surfaces with Boundary\"\"; \"\"Chapter 2: Pseudoholomorphic Curves\"\"; \"\"2.1 Basic De nitions\"\"; \"\"2.2 Asymptotic Behavior Near a Puncture\"\"; \"\"2.2.1 Introduction\"\"; \"\"2.2.2 Estimates for the Linear Cauchy Riemann Operator\"\"; \"\"2.2.3 Regularity: Gradient Bounds Imply Cinfty-Bounds\"\"; \"\"2.2.4 Behavior Near an Interior Puncture\"\"; \"\"2.2.5 Behavior Near a Boundary Puncture\"\"; \"\"2.3 Isoperimetric Inequality, Monotonicity Lemma, Removal of Singularities\"\""]
[{"$nulls":4},"\"\"2.4 Finite-Energy Strips and Cylinders of Small Area\"\"\"\"Chapter 3: The SFT Compactness Results\"\"; \"\"3.1 Holomorphic Buildings for Curves Without Boundary\"\"; \"\"3.1.1 Holomorphic Buildings of Height 1\"\"; \"\"3.1.2 Holomorphic Buildings of Height N\"\"; \"\"3.2 Adding Additional Marked Points\"\"; \"\"3.3 The Compactness Result for the Case Without "]
[{"$nulls":4},"Boundary\"\"; \"\"3.3.1 Statement of the Result\"\"; \"\"3.3.2 Gradient Bounds\"\"; \"\"3.3.3 Convergence in the Thick Part\"\"; \"\"3.3.4 Convergence in the Thin Part and Level Structure\"\"; \"\"3.4 More General Holomorphic Buildings and Compactness Results\"\""]
[{"$nulls":4},"\"\"3.4.1 Holomorphic Buildings of Height 1\"\"\"\"3.4.2 Holomorphic Buildings of Height N\"\"; \"\"3.4.3 Holomorphic Buildings in Manifolds with Cylindrical Ends\"\"; \"\"3.4.4 A More General Compactness Result\"\"; \"\"References\"\"; \"\"Index\"\""]
[null,null,null,"Sommario/riassunto","This book provides an introduction to symplectic field theory, a new and important subject which is currently being developed. The starting point of this theory are compactness results for holomorphic curves established in the last decade. The author presents a systematic introduction providing a lot of background material, much of which is scattered throughout the literature. Since the content grew out of lectures given by the author, the main aim is to provide an entry point into symplectic field theory for non-specialists and for graduate students. Extensions of certain compactness results, which are believed to be true by the specialists but have not yet been published in the literature in detail, top off the scope of this monograph."]
[null,"3.","Record Nr.",null,"UNINA9910754092903321"]
[null,null,"Autore",null,"Pilz Jürgen"]
[null,null,"Titolo",null,"Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications : Selected Contributions from SimStat 2019 and Invited Papers / / edited by Jürgen Pilz, Viatcheslav B. Melas, Arne Bathke"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"9783031400551"]
[{"$nulls":4},"3031400550"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (265 pages)"]
[null,null,"Collana",null,"Contributions to Statistics, , 2628-8966"]
[null,null,"Disciplina",null,"519.57"]
[null,null,"Soggetti",null,"Statistics"]
[{"$nulls":4},"Mathematical statistics - Data processing"]
[{"$nulls":4},"Experimental design"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Stochastic models"]
[{"$nulls":4},"Statistical Theory and Methods"]
[{"$nulls":4},"Statistics and Computing"]
[{"$nulls":4},"Design of Experiments"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Applied Statistics"]
[{"$nulls":4},"Stochastic Modelling in Statistics"]
[{"$nulls":4},"Disseny d'experiments"]
[{"$nulls":4},"Aprenentatge automàtic"]
[{"$nulls":4},"Estadística matemàtica"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- Part I Invited Papers -- 1 Likelihood Ratios in Forensics: What They Are and What They Are Not -- 1.1 Introduction -- 1.2 Lindley's Likelihood Ratio (LLR) -- 1.2.1 Notations -- 1.2.2 A Frequentist Framework for Lindley's Likelihood Ratio (LLR) -- 1.3 Score-Based Likelihood Ratio (SLR) -- 1.3.1 The Expression of the SLR -- 1.3.2 The Glass Example -- 1.4 Discussion -- References -- 2 "]
[{"$nulls":4},"MANOVA for Large Number of Treatments -- 2.1 Introduction -- 2.2 Notations and Model Setup -- 2.3 Simulations -- 2.3.1 MANOVA Tests for Large g -- 2.3.2 Special Case: ANOVA for Large g -- 2.4 Discussion and Outlook -- References -- 3 Pollutant Dispersion Simulation by Means of a Stochastic Particle Model and a Dynamic Gaussian Plume Model -- 3.1 Introduction -- 3.2 Meteorological Monitoring Network -- 3.3 Wind Field Modeling -- 3.3.1 Mass Correction of the Wind Field -- 3.3.2 Plume Rise -- 3.4 Stochastic Particle Model -- 3.4.1 Deposition -- 3.4.2 Implementation -- 3.5 Dynamic Gaussian Plume Model -- 3.6 Implementation on the Server -- 3.7 A Real-World Example with Application to an Alpine Valley -- 3.8 Conclusions and Outlook -- References -- 4 On an Alternative Trigonometric Strategy for StatisticalModeling -- 4.1 Introduction -- 4.2 The Alternative Sine Distribution -- 4.2.1 Presentation -- 4.2.2 Moment Properties -- 4.2.3 Parametric Extensions -- 4.3 AS Generated Family -- 4.3.1 Definition -- 4.3.2 Series Expansions -- 4.3.3 Example: The ASE Exponential Distribution -- 4.3.4 Moment Properties -- 4.4 Application to a Famous Cancer Data -- 4.5 Conclusion -- References -- Part II Design of Experiments -- 5 Incremental Construction of Nested Designs Basedon Two-Level Fractional Factorial Designs -- 5.1 Introduction -- 5.2 Greedy Coffee-House Design -- 5.3 Two-Level Fractional Factorial Designs -- 5.3.1 Half Fractions: m=1."]
[{"$nulls":4},"5.3.2 Several Generators -- 5.3.2.1 Defining Relations -- 5.3.2.2 Resolution -- 5.3.2.3 Word Length Pattern -- 5.3.3 Minimum Size -- 5.4 Two-Level Factorial Designs and Error-Correcting Codes -- 5.4.1 Definitions and Properties -- 5.4.2 Examples -- 5.5 Maximin Distance Properties of Two-Level Factorial Designs -- 5.5.1 Neighbouring Pattern and Distant Site Pattern -- 5.5.2 Optimal Selection of Generators by Simulated Annealing -- 5.5.2.1 SA Algorithm for the Maximisation of ρH -- 5.6 Covering Properties of Two-Level Factorial Designs -- 5.6.1 Bounds on CRH(Xn) -- 5.6.2 Calculation of CRH(Xn) -- 5.6.2.1 Algorithmic Construction of a Lower Bound on CRH(Xn) -- 5.7 Greedy Constructions Based on Fractional Factorial Designs -- 5.7.1 Base Designs -- 5.7.2 Rescaled Designs -- 5.7.3 Projection Properties -- 5.8 Summary and Future Work -- Appendix -- References -- 6 A Study of L-Optimal Designs for the Two-Dimensional Exponential Model -- 6.1 Introduction -- 6.2 Equivalence Theorem for L-Optimal Designs -- 6.3 General Case -- 6.4 Excess and Saturated Designs -- References -- 7 Testing for Randomized Block Single-Case Designsby Combined Permutation Tests with Multivariate Mixed Data -- 7.1 Introduction -- 7.2 Randomized Block Single-Case Designs and NPC -- 7.3 Simulation Study -- 7.4 A Real Case Study -- 7.5 Conclusions -- References -- 8 Adaptive Design Criteria Motivated by a Plug-In Percentile Estimator -- 8.1 Introduction -- 8.2 Problem Formulation and Background -- 8.2.1 Problem Formulation -- 8.2.2 Background -- 8.3 The Plug-In Estimator -- 8.4 Adaptive ``Plug-In'' Criteria -- 8.4.1 Monte Carlo Approximation -- 8.4.2 Monte Carlo Approximation Assuming Independency -- 8.4.3 Assuming Independency and Neglecting Uncertainty -- 8.4.4 Using SUR Design Criterion for Exceedance Probability -- 8.5 Numerical Implementation -- 8.6 Numerical Study."]
[{"$nulls":4},"8.6.1 Comparison Study -- 8.6.2 Methodology -- 8.6.2.1 Case Studies -- 8.6.2.2 Performance Indicators -- 8.6.3 Numerical Results -- 8.6.3.1 Estimators Performance -- 8.6.3.2 Implementation -- 8.6.3.3 Criteria -- 8.7 Conclusions -- Appendix 1 -- Posterior Mean and Variance of f Under the Gaussian Process Assumption -- SUR Design Criteria for Exceedance Probability Estimation -- Appendix 2 -- References -- Part III Queueing and Inventory Analysis -- 9 On a "]
[{"$nulls":4},"Parametric Estimation for a Convolutionof Exponential Densities -- 9.1 Introduction -- 9.2 Convolution of the Exponential Densities -- 9.3 ML Estimation of the Parameters -- 9.4 Parameter's Estimation by the Moments' Method -- 9.5 Approximation of the Density -- 9.6 Experimental Study -- 9.7 Application to a Single Queueing System M/G/1/k -- 9.8 Conclusions -- References -- 10 Statistical Estimation with a Known Quantileand Its Application in a Modified ABC-XYZ Analysis -- 10.1 Introduction -- 10.2 Methods -- 10.2.1 Statistical Estimation with a Known Quantile -- 10.2.2 ABC-XYZ Analysis -- 10.3 ABC-XYZ Analysis Modified with a Known Quantile -- 10.4 Conclusions -- References -- Part IV Machine Learning and Applications -- 11 A Study of Design of Experiments and Machine Learning Methods to Improve Fault Detection Algorithms -- 11.1 Introduction -- 11.2 Design of Experiments and Machine Learning Modelling -- 11.3 Application to Fault Detection -- 11.3.1 Design of Experiments Step -- 11.3.2 Machine Learning Modelling Step -- 11.3.2.1 Refrigerant Undercharge: Fault Detection -- 11.3.2.2 Condenser Fouling: Fault Detection -- 11.4 Conclusions -- References -- 12 Microstructure Image Segmentation Using Patch-Based Clustering Approach -- 12.1 Introduction -- 12.2 Input Data -- 12.3 Previous Work -- 12.4 Grain Segmentation -- 12.4.1 Seeded Region Growing (SRG) -- 12.4.2 Image Denoising and Patch Determination."]
[{"$nulls":4},"12.4.3 Feature Extraction -- 12.4.4 Patch Clustering -- 12.4.5 Implementation -- 12.5 Results -- 12.6 Conclusion and Outlook -- References -- 13 Clustering and Symptom Analysis in Binary Datawith Application -- 13.1 Introduction -- 13.2 The Symptom Analysis -- 13.2.1 The Symptom and Syndrome Definition -- 13.2.2 Impulse Vector and Super-symptoms -- 13.2.3 Prefigurations of Super-symptom -- 13.2.4 The Super-symptom Recovery by Vector β -- 13.2.5  Clustering in Dichotomous Space and Symptom Analysis -- 13.3 The Medical Application of the Clustering and Symptom Analysis in Binary Data -- 13.3.1 Dataset -- 13.3.2 Result and Discussion -- 13.4 Conclusion -- References -- 14 Big Data for Credit Risk Analysis: Efficient Machine Learning Models Using PySpark -- 14.1 Introduction -- 14.2 Data Processing -- 14.2.1 Data Treatment -- 14.2.2 Data Storage and Distribution -- 14.2.3 Munge Data -- 14.2.4 Creating New Measures -- 14.2.5 Missing Values Imputation and Outliers Treatment -- 14.2.6 One-Hot Code and Dummy Variables -- 14.2.7 Final Dataset -- 14.3 Method and Models -- 14.3.1 Method -- 14.3.2 Model Building -- 14.4 Results and Credit Scorecard Conversion -- 14.5 Conclusion -- Appendix 1 -- Appendix 2 -- References."]
[null,null,null,"Sommario/riassunto","This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications. The refereed contributions originate from the 10th International Workshop on Simulation and Statistics, SimStat 2019, which was held in Salzburg, Austria, September 2–6, 2019, and were either presented at the conference or developed afterwards, relating closely to the topics of the workshop. The book is intended for statisticians and Ph.D. students who seek current developments and applications in the field."]