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1. |
Record Nr. |
UNINA9910463225803321 |
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Autore |
Martinez Joel |
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Titolo |
C# 5 first look [[electronic resource] ] : write ultra responsive applications using the new asynchronous features of C# / / Joel Martinez |
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Pubbl/distr/stampa |
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Birmingham, UK, : Packt Pub., 2012 |
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ISBN |
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1-283-93786-7 |
1-84968-677-7 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (139 p.) |
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Disciplina |
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Soggetti |
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C# (Computer program language) |
Object-oriented programming languages |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with C#; Origins; C# is born; The tools; Visual Studio; Full versions of Visual Studio; Licensing; Express; Using Visual Studio; Summary; Chapter 2: Evolution of C#; C# 1.0-in the beginning; Runtime; Memory management; Syntax features; Base Class Library; C# 2.0; Syntax updates; Anonymous methods; Partial classes; Generics; Generic collections; Iterator methods; C# 3.0; Syntax updates; LINQ; Extension methods; C# 4.0; Summary |
Chapter 3: Asynchrony in ActionAsynchrony; Task Parallel Library; Task composability; Error handling with tasks; async and await; Composing async calls; Error handling with async methods; Impact of async; Improvements in NET 4.5 Framework; TPLDataFlow; ActionBlock; TransformBlock; BatchBlock; BroadcastBlock; Async I/O; Caller attributes; Summary; Chapter 4: Creating a Windows Store App; Making a Flickr browser; Getting the project started; Connecting to Flickr; Creating the UI; Summary; Chapter 5: Mobile Web App; Mobile Web with ASP.NET MVC; Building a MeatSpace tracker; Iteration zero |
Going asynchronousGetting the user's location; Broadcasting with SignalR; Mapping users; Testing the app; Summary; Chapter 6: Cross- |
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platform Development; Building a web scraper; Building the model; Accessing the Web; Making a DataSource; Building the view; Summary; Index |
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This will be a mix of concept introduction and examples, and with each new feature and enhancement we will give an example to the readers. C# 5 First Look will provide a gist of C# 5 to the readers. ""C# 5 First Look"" is for developers who want to learn about the latest version of C#. It is assumed that you have basic programming knowledge. Experience with prior versions of C# or the .NET Framework would be helpful, but not mandatory. |
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2. |
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UNINA9910143555803321 |
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Autore |
Wu Hulin |
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Titolo |
Nonparametric regression methods for longitudinal data analysis [[electronic resource] ] : [mixed-effects modeling approaches] / / Hulin Wu, Jin-Ting Zhang |
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Pubbl/distr/stampa |
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Hoboken, N.J., : Wiley-Interscience, c2006 |
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ISBN |
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1-280-44838-5 |
9786610448388 |
0-470-00967-5 |
0-470-00966-7 |
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Descrizione fisica |
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1 online resource (401 p.) |
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Collana |
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Wiley series in probability and statistics |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Nonparametric statistics |
Longitudinal method - Mathematical models |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references (p. 347-361) and index. |
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Nota di contenuto |
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Nonparametric Regression Methods for Longitudinal Data Analysis; Preface; Contents; Acronyms; 1 Introduction; 1.1 Motivating Longitudinal Data Examples; 1.1.1 Progesterone Data; 1.1.2 ACTG 388 Data; 1.1.3 MACS Data; 1.2 Mixed-Effects Modeling: from Parametric to |
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Nonparametric; 1.2.1 Parametric Mixed-Effects Models; 1.2.2 Nonparametric Regression and Smoothing; 1.2.3 Nonparametric Mixed-Effects Models; 1.3 Scope of the Book; 1.3.1 Building Blocks of the NPME Models; 1.3.2 Fundamental Development of the NPME Models; 1.3.3 Further Extensions of the NPME Models |
1.4 Implementation of Methodologies1.5 Options for Reading This Book; 1.6 Bibliographical Notes; 2 Parametric Mixed-Effects Models; 2.1 Introduction; 2.2 Linear Mixed-Effects Model; 2.2.1 Model Specification; 2.2.2 Estimation of Fixed and Random-Effects; 2.2.3 Bayesian Interpretation; 2.2.4 Estimation of Variance Components; 2.2.5 The EM-Algorithms; 2.3 Nonlinear Mixed-Effects Model; 2.3.1 Model Specification; 2.3.2 Two-Stage Method; 2.3.3 First-Order Linearization Method; 2.3.4 Conditional First-Order Linearization Method; 2.4 Generalized Mixed-Effects Model |
2.4.1 Generalized Linear Mixed-Effects Model2.4.2 Examples of GLME Model; 2.4.3 Generalized Nonlinear Mixed-Effects Model; 2.5 Summary and Bibliographical Notes; 2.6 Appendix: Proofs; 3 Nonparametric Regression Smoothers; 3.1 Introduction; 3.2 Local Polynomial Kernel Smoother; 3.2.1 General Degree LPK Smoother; 3.2.2 Local Constant and Linear Smoothers; 3.2.3 Kernel Function; 3.2.4 Bandwidth Selection; 3.2.5 An Illustrative Example; 3.3 Regression Splines; 3.3.1 Truncated Power Basis; 3.3.2 Regression Spline Smoother; 3.3.3 Selection of Number and Location of Knots |
3.3.4 General Basis-Based Smoother3.4 Smoothing Splines; 3.4.1 Cubic Smoothing Splines; 3.4.2 General Degree Smoothing Splines; 3.4.3 Connection between a Smoothing Spline and a LME Model; 3.4.4 Connection between a Smoothing Spline and a State-Space Model; 3.4.5 Choice of Smoothing Parameters; 3.5 Penalized Splines; 3.5.1 Penalized Spline Smoother; 3.5.2 Connection between a Penalized Spline and a LME Model; 3.5.3 Choice of the Knots and Smoothing Parameter Selection; 3.5.4 Extension; 3.6 Linear Smoother; 3.7 Methods for Smoothing Parameter Selection; 3.7.1 Goodness of Fit |
3.7.2 Model Complexity3.7.3 Cross-Validation; 3.7.4 Generalized Cross-Validation; 3.7.5 Generalized Maximum Likelihood; 3.7.6 Akaike Information Criterion; 3.7.7 Bayesian Information Criterion; 3.8 Summary and Bibliographical Notes; 4 Local Polynomial Methods; 4.1 Introduction; 4.2 Nonparametric Population Mean Model; 4.2.1 Naive Local Polynomial Kernel Method; 4.2.2 Local Polynomial Kernel GEE Method; 4.2.3 Fan-Zhang 's Two-step Method; 4.3 Nonparametric Mixed-Effects Model; 4.4 Local Polynomial Mixed-Effects Modeling; 4.4.1 Local Polynomial Approximation; 4.4.2 Local Likelihood Approach |
4.4.3 Local Marginal Likelihood Estimation |
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Sommario/riassunto |
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Incorporates mixed-effects modeling techniques for more powerful and efficient methodsThis book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented.With its logical structure and organization, beginning with basic principles, the text develops t |
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3. |
Record Nr. |
UNINA9910842993803321 |
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Autore |
Barbera, Gianluca |
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Titolo |
Basta odio : la rivoluzione gentile / Gianluca Barbera |
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Pubbl/distr/stampa |
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Reggio Emilia, : Compagnia editoriale Aliberti, 2023 |
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ISBN |
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Descrizione fisica |
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Collana |
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I libri della salamandra. Extra+ ; 29 |
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Disciplina |
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Locazione |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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