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An introduction to bibliometrics : new developments and trends / / Rafael Ball
| An introduction to bibliometrics : new developments and trends / / Rafael Ball |
| Autore | Ball Rafael |
| Pubbl/distr/stampa | Cambridge, Massachusetts : , : Chandos Publishing, , 2018 |
| Descrizione fisica | 1 online resource (92 pages) |
| Disciplina | 010.727 |
| Collana | Chandos Information Professional Series |
| Soggetto topico |
Bibliometrics
Information science - Statistical methods |
| ISBN |
0-08-102151-8
0-08-102150-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910583377603321 |
Ball Rafael
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| Cambridge, Massachusetts : , : Chandos Publishing, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Introduction to statistical machine learning / / Masashi Sugiyama
| Introduction to statistical machine learning / / Masashi Sugiyama |
| Autore | Sugiyama Masashi <1974-> |
| Pubbl/distr/stampa | Amsterdam : , : Elsevier, , [2016] |
| Descrizione fisica | 1 online resource (535 p.) |
| Disciplina | 006.3/1 |
| Soggetto topico |
Machine learning - Statistical methods
Information science - Statistical methods Pattern recognition systems |
| ISBN | 0-12-802350-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Introduction to Statistical Machine Learning; Copyright; Table of Contents; Biography; Preface; 1 INTRODUCTION; 1 Statistical Machine Learning; 1.1 Types of Learning; 1.2 Examples of Machine Learning Tasks; 1.2.1 Supervised Learning; 1.2.2 Unsupervised Learning; 1.2.3 Further Topics; 1.3 Structure of This Textbook; 2 STATISTICS AND PROBABILITY; 2 Random Variables and Probability Distributions; 2.1 Mathematical Preliminaries; 2.2 Probability; 2.3 Random Variable and Probability Distribution; 2.4 Properties of Probability Distributions; 2.4.1 Expectation, Median, and Mode
2.4.2 Variance and Standard Deviation2.4.3 Skewness, Kurtosis, and Moments; 2.5 Transformation of Random Variables; 3 Examples of Discrete Probability Distributions; 3.1 Discrete Uniform Distribution; 3.2 Binomial Distribution; 3.3 Hypergeometric Distribution; 3.4 Poisson Distribution; 3.5 Negative Binomial Distribution; 3.6 Geometric Distribution; 4 Examples of Continuous Probability Distributions; 4.1 Continuous Uniform Distribution; 4.2 Normal Distribution; 4.3 Gamma Distribution, Exponential Distribution, and Chi-Squared Distribution; 4.4 Beta Distribution 4.5 Cauchy Distribution and Laplace Distribution4.6 t-Distribution and F-Distribution; 5 Multidimensional Probability Distributions; 5.1 Joint Probability Distribution; 5.2 Conditional Probability Distribution; 5.3 Contingency Table; 5.4 Bayes' Theorem; 5.5 Covariance and Correlation; 5.6 Independence; 6 Examples of Multidimensional Probability Distributions; 6.1 Multinomial Distribution; 6.2 Multivariate Normal Distribution; 6.3 Dirichlet Distribution; 6.4 Wishart Distribution; 7 Sum of Independent Random Variables; 7.1 Convolution; 7.2 Reproductive Property; 7.3 Law of Large Numbers 7.4 Central Limit Theorem8 Probability Inequalities; 8.1 Union Bound; 8.2 Inequalities for Probabilities; 8.2.1 Markov's Inequality and Chernoff's Inequality; 8.2.2 Cantelli's Inequality and Chebyshev's Inequality; 8.3 Inequalities for Expectation; 8.3.1 Jensen's Inequality; 8.3.2 Hölder's Inequality and Schwarz's Inequality; 8.3.3 Minkowski's Inequality; 8.3.4 Kantorovich's Inequality; 8.4 Inequalities for the Sum of Independent Random Variables; 8.4.1 Chebyshev's Inequality and Chernoff's Inequality; 8.4.2 Hoeffding's Inequality and Bernstein's Inequality; 8.4.3 Bennett's Inequality 9 Statistical Estimation9.1 Fundamentals of Statistical Estimation; 9.2 Point Estimation; 9.2.1 Parametric Density Estimation; 9.2.2 Nonparametric Density Estimation; 9.2.3 Regression and Classification; 9.2.4 Model Selection; 9.3 Interval Estimation; 9.3.1 Interval Estimation for Expectation of Normal Samples; 9.3.2 Bootstrap Confidence Interval; 9.3.3 Bayesian Credible Interval; 10 Hypothesis Testing; 10.1 Fundamentals of Hypothesis Testing; 10.2 Test for Expectation of Normal Samples; 10.3 Neyman-Pearson Lemma; 10.4 Test for Contingency Tables 10.5 Test for Difference in Expectations of Normal Samples |
| Record Nr. | UNINA-9910583088403321 |
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Sugiyama Masashi <1974->
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| Amsterdam : , : Elsevier, , [2016] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Journal of informetrics
| Journal of informetrics |
| Pubbl/distr/stampa | [Amsterdam], : Elsevier Science |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Library statistics
Information science - Statistical methods Bibliometrics Statistics as Topic Bibliothèques - Statistiques Sciences de l'information - Méthodes statistiques Bibliométrie |
| Soggetto genere / forma |
Periodicals.
Periodical |
| Soggetto non controllato | Library & Information Science |
| ISSN | 1875-5879 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996197686503316 |
| [Amsterdam], : Elsevier Science | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Journal of informetrics
| Journal of informetrics |
| Pubbl/distr/stampa | [Amsterdam], : Elsevier Science |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Library statistics
Information science - Statistical methods Bibliometrics Statistics Bibliothèques - Statistiques Sciences de l'information - Méthodes statistiques Bibliométrie |
| Soggetto genere / forma | Periodicals. |
| ISSN | 1875-5879 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910144883503321 |
| [Amsterdam], : Elsevier Science | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Power laws in the information production process [[recurso electrónico] /] / edited by Leo Egghe
| Power laws in the information production process [[recurso electrónico] /] / edited by Leo Egghe |
| Pubbl/distr/stampa | Amsterdam ; ; New York, : Elsevier/Academic Press, 2005 |
| Descrizione fisica | 1 online resource (447 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) | EggheL (Leo) |
| Collana | Library and information science |
| Soggetto topico |
Business & Economics - Economics - General
Social Science - Sociology - General Probability & statistics Bibliometrics Library statistics Information science - Statistical methods |
| ISBN |
1-281-00809-5
9786611008093 1-4237-0928-4 1-84950-802-X 0-08-048011-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Basic theory of Lotkaian informetrics / Leo Egghe -- Three-dimensional Lotkaian informetrics / Leo Egghe -- Lotkaian concentration theory / Leo Egghe -- Lotkaian fractal complexity theory / Leo Egghe -- Lotkaian informetrics of systems in which items can have multiple sources / Leo Egghe -- Further applications in Lotkaian informetrics / Leo Egghe -- Lotkaian informetrics : an introduction / Leo Egghe. |
| Record Nr. | UNINA-9910671319203321 |
| Amsterdam ; ; New York, : Elsevier/Academic Press, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Probability in the engineering and informational sciences
| Probability in the engineering and informational sciences |
| Pubbl/distr/stampa | [Cambridge, England], : Cambridge University Press, 1987- |
| Disciplina | 620.0042 |
| Soggetto topico |
Engineering - Statistical methods
Information science - Statistical methods Probabilities |
| ISSN | 1469-8951 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996332649603316 |
| [Cambridge, England], : Cambridge University Press, 1987- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Probability in the engineering and informational sciences
| Probability in the engineering and informational sciences |
| Pubbl/distr/stampa | [Cambridge, England], : Cambridge University Press, 1987- |
| Descrizione fisica | 1 online resource |
| Disciplina | 620.0042 |
| Soggetto topico |
Engineering - Statistical methods
Information science - Statistical methods Probabilities |
| Soggetto genere / forma | Periodicals. |
| ISSN | 1469-8951 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910132869403321 |
| [Cambridge, England], : Cambridge University Press, 1987- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Reality mining : using big data to engineer a better world / / by Nathan Eagle and Kate Greene
| Reality mining : using big data to engineer a better world / / by Nathan Eagle and Kate Greene |
| Autore | Eagle Nathan |
| Pubbl/distr/stampa | Cambridge, Massachusetts : , : MIT Press, , [2014] |
| Descrizione fisica | 1 online resource (207 p.) |
| Disciplina | 006.3/12 |
| Soggetto topico |
Data mining
Big data Computer networks - Social aspects Information science - Social aspects Information science - Statistical methods |
| ISBN |
0-262-32457-1
0-262-52983-1 0-262-32456-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Contents; Introduction; I The Individual (One Person); 1 Mobile Phones, Sensors, and Lifelogging: Collecting Data from Individuals While Considering Privacy; 2 Using Personal Data in a Privacy-Sensitive Way to Make a Person's Life Easier and Healthier; II The Neighborhood and the Organization (10 to 1,000 People); 3 Gathering Data from Small HeterogeneousGroups; 4 Engineering and Policy: Building More EfficientBusinesses, Enabling Hyperlocal Politics, LifeQueries, and Opportunity Searches; III The City (1,000 to 1,000,000 People)
5 Traffic Data, Crime Stats, and Closed-Circuit Cameras: Accumulating Urban Analytics6 Engineering and Policy: Optimizing Resource Allocation; IV The Nation (1 Million to 100 Million People); 7 Taking the Pulse of a Nation: Census, Mobile Phones, and Internet Giants; 8 Engineering and Policy: Addressing National Sentiment, Economic Deficits, and Disasters; V Reality Mining the World's Data (100 Million to 7 Billion People); 9 Gathering the World's Data: Global Census, International Travel and Commerce, and Planetary-Scale Communication; 10 Engineering a Safer and Healthier World; Conclusion NotesIndex |
| Record Nr. | UNINA-9910786896703321 |
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Eagle Nathan
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| Cambridge, Massachusetts : , : MIT Press, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Reality mining : using big data to engineer a better world / / by Nathan Eagle and Kate Greene
| Reality mining : using big data to engineer a better world / / by Nathan Eagle and Kate Greene |
| Autore | Eagle Nathan |
| Pubbl/distr/stampa | Cambridge, Massachusetts : , : MIT Press, , [2014] |
| Descrizione fisica | 1 online resource (207 p.) |
| Disciplina | 006.3/12 |
| Soggetto topico |
Data mining
Big data Computer networks - Social aspects Information science - Social aspects Information science - Statistical methods |
| ISBN |
0-262-32457-1
0-262-52983-1 0-262-32456-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Contents; Introduction; I The Individual (One Person); 1 Mobile Phones, Sensors, and Lifelogging: Collecting Data from Individuals While Considering Privacy; 2 Using Personal Data in a Privacy-Sensitive Way to Make a Person's Life Easier and Healthier; II The Neighborhood and the Organization (10 to 1,000 People); 3 Gathering Data from Small HeterogeneousGroups; 4 Engineering and Policy: Building More EfficientBusinesses, Enabling Hyperlocal Politics, LifeQueries, and Opportunity Searches; III The City (1,000 to 1,000,000 People)
5 Traffic Data, Crime Stats, and Closed-Circuit Cameras: Accumulating Urban Analytics6 Engineering and Policy: Optimizing Resource Allocation; IV The Nation (1 Million to 100 Million People); 7 Taking the Pulse of a Nation: Census, Mobile Phones, and Internet Giants; 8 Engineering and Policy: Addressing National Sentiment, Economic Deficits, and Disasters; V Reality Mining the World's Data (100 Million to 7 Billion People); 9 Gathering the World's Data: Global Census, International Travel and Commerce, and Planetary-Scale Communication; 10 Engineering a Safer and Healthier World; Conclusion NotesIndex |
| Record Nr. | UNINA-9910807063703321 |
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Eagle Nathan
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| Cambridge, Massachusetts : , : MIT Press, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Statistical and machine learning approaches for network analysis / / edited by Matthias Dehmer, Subhash C. Basak
| Statistical and machine learning approaches for network analysis / / edited by Matthias Dehmer, Subhash C. Basak |
| Autore | Dehmer Matthias <1968-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2012 |
| Descrizione fisica | 1 online resource (345 p.) |
| Disciplina | 511/.5 |
| Altri autori (Persone) |
DehmerMatthias <1968->
BasakSubhash C. <1945-> |
| Collana | Wiley series in computational statistics |
| Soggetto topico |
Research - Statistical methods
Machine theory Communication - Network analysis - Graphic methods Information science - Statistical methods |
| ISBN |
9786613714022
9781280872716 1280872713 9781118346983 111834698X 9781118346990 1118346998 9781118347010 1118347013 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistical and Machine Learning Approaches for Network Analysis; Contents; Preface; Contributors; 1 A Survey of Computational Approaches to Reconstruct and Partition Biological Networks; 1.1 INTRODUCTION; 1.2 BIOLOGICAL NETWORKS; 1.2.1 Directed Networks; 1.2.2 Undirected Networks; 1.3 GENOME-WIDE MEASUREMENTS; 1.3.1 Gene Expression Data; 1.3.2 Gene Sets; 1.4 RECONSTRUCTION OF BIOLOGICAL NETWORKS; 1.4.1 Reconstruction of Directed Networks; 1.4.1.1 Boolean Networks; 1.4.1.2 Probabilistic Boolean Networks; 1.4.1.3 Bayesian Networks; 1.4.1.4 Collaborative Graph Model; 1.4.1.5 Frequency Method
1.4.1.6 EM-Based Inference from Gene Sets1.4.2 Reconstruction of Undirected Networks; 1.4.2.1 Relevance Networks; 1.4.2.2 Graphical Gaussian Models; 1.5 PARTITIONING BIOLOGICAL NETWORKS; 1.5.1 Directed and Undirected Networks; 1.5.2 Partitioning Undirected Networks; 1.5.2.1 Kernighan-Lin Algorithm; 1.5.2.2 Girvan-Newman Algorithm; 1.5.2.3 Newman's Eigenvector Method; 1.5.2.4 Infomap; 1.5.2.5 Clique Percolation Method; 1.5.3 Partitioning Directed Networks; 1.5.3.1 Newman's Eigenvector Method; 1.5.3.2 Infomap; 1.5.3.3 Clique Percolation Method; 1.6 DISCUSSION; REFERENCES 2 Introduction to Complex Networks: Measures, Statistical Properties, and Models2.1 INTRODUCTION; 2.2 REPRESENTATION OF NETWORKS; 2.3 CLASSICAL NETWORK; 2.3.1 Random Network; 2.3.2 Lattice Network; 2.4 SCALE-FREE NETWORK; 2.4.1 Degree Distribution; 2.4.2 Degree Distribution of Random Network; 2.4.3 Power-Law Distribution in Real-World Networks; 2.4.4 Barab ́asi-Albert Model; 2.4.5 Configuration Model; 2.5 SMALL-WORLD NETWORK; 2.5.1 Average Shortest Path Length; 2.5.2 Ultrasmall-World Network; 2.6 CLUSTERED NETWORK; 2.6.1 Clustering Coefficient; 2.6.2 Watts-Strogatz Model 2.7 HIERARCHICAL MODULARITY2.7.1 Hierarchical Model; 2.7.2 Dorogovtsev-Mendes-Samukhin Model; 2.8 NETWORK MOTIF; 2.9 ASSORTATIVITY; 2.9.1 Assortative Coefficient; 2.9.2 Degree Correlation; 2.9.3 Linear Preferential Attachment Model; 2.9.4 Edge Rewiring Method; 2.10 RECIPROCITY; 2.11 WEIGHTED NETWORKS; 2.11.1 Strength; 2.11.2 Weighted Clustering Coefficient; 2.11.3 Weighted Degree Correlation; 2.12 NETWORK COMPLEXITY; 2.13 CENTRALITY; 2.13.1 Definition; 2.13.2 Comparison of Centrality Measures; 2.14 CONCLUSION; REFERENCES; 3 Modeling for Evolving Biological Networks; 3.1 INTRODUCTION 3.2 UNIFIED EVOLVING NETWORK MODEL: REPRODUCTION OF HETEROGENEOUS CONNECTIVITY, HIERARCHICAL MODULARITY, AND DISASSORTATIVITY3.2.1 Network Model; 3.2.2 Degree Distribution; 3.2.3 Degree-Dependent Clustering Coefficient; 3.2.4 Average Clustering Coefficient; 3.2.5 Degree Correlation; 3.2.6 Assortative Coefficient; 3.2.7 Comparison with Real Data; 3.3 MODELING WITHOUT PARAMETER TUNING: A CASE STUDY OF METABOLIC NETWORKS; 3.3.1 Network Model; 3.3.2 Analytical Solution; 3.3.3 Estimation of the Parameters; 3.3.4 Comparison with Real Data 3.4 BIPARTITE RELATIONSHIP: A CASE STUDY OF METABOLITE DISTRIBUTION |
| Record Nr. | UNINA-9910141263803321 |
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Dehmer Matthias <1968->
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| Hoboken, N.J., : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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