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[null,"1.","Record Nr.",null,"UNINA9910338003903321"]
[null,null,"Autore",null,"Voss Michael"]
[null,null,"Titolo",null,"Pro TBB [[electronic resource] ] : C++ Parallel Programming with Threading Building Blocks / / by Michael Voss, Rafael Asenjo, James Reinders"]
[null,null,"Pubbl/distr/stampa",null,"Berkeley, CA, : Springer Nature, 2019"]
[{"$nulls":4},"Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019"]
[null,null,"ISBN",null,"1-4842-4398-6"]
[null,null,"Edizione",null,"[1st ed. 2019.]"]
[null,null,"Descrizione fisica",null,"1 online resource (LXVI, 754 p. 614 illus., 460 illus. in color.)"]
[null,null,"Disciplina",null,"005.13"]
[null,null,"Soggetti",null,"Programming languages (Electronic computers)"]
[{"$nulls":4},"Computer programming"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Data structures (Computer science)"]
[{"$nulls":4},"Programming Languages, Compilers, Interpreters"]
[{"$nulls":4},"Programming Techniques"]
[{"$nulls":4},"Algorithm Analysis and Problem Complexity"]
[{"$nulls":4},"Data Structures"]
[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,"Part I -- Chapter 1: Jumping Right In – “Hello, TBB!” -- Chapter 2: Generic Parallel Algorithms -- Chapter 3: Flow Graphs -- Chapter 4: TBB and the C++ Parallel Standard Template Library -- Chapter 5: Synchronization: why and how to avoid it -- Chapter 6: Data Structures for Concurrency -- Chapter 7: Scalable Memory Allocation -- Chapter 8: Mapping Parallel Patterns to TBB -- Part II -- Chapter 9: The Pillars of Composability -- Chapter 10: Using tasks to create your own algorithms -- Chapter 11: Controlling the Number of Threads Used for Execution -- Chapter 12: Using Work Isolation for Correctness and Performance -- Chapter 13: Creating Thread-to-core and Task-to-thread Affinity -- Chapter 14: Using Task Priorities -- Chapter 15: Cancellation and Exception Handling -- Chapter 16: Tuning TBB Algorithms: Granularity, Locality, Parallelism and Determinism -- Chapter 17: Flow Graphs: Beyond the Basics -- Chapter 18: Beef up Flow Graphs with Async Nodes -- Chapter 19: Flow Graphs on steroids: OpenCL Nodes -- Chapter 20: TBB on NUMA architectures -- Appendix "]
[{"$nulls":4},"A: History and Inspiration -- Appendix B: TBB Précis -- Glossary. ."]
[null,null,null,"Sommario/riassunto","This open access book is a modern guide for all C++ programmers to learn Intel Threading Building Blocks (TBB). Written by TBB and parallel programming experts, this book reflects their collective decades of experience in developing and teaching parallel programming with TBB, offering their insights in an approachable manner. Throughout the book the authors present numerous examples and best practices to help you become an effective TBB programmer and leverage the power of parallel systems. Pro Intel Threading Building Blocks starts with the basics, explaining parallel algorithms and C++'s built-in standard template library for parallelism. You'll learn the key concepts of managing memory, working with data structures and how to handle typical issues with synchronization. Later chapters apply these ideas to complex systems to explain performance tradeoffs, mapping common parallel patterns, controlling threads and overhead, and extending TBB to program heterogeneous systems or system-on-chips. ."]
[null,"2.","Record Nr.",null,"UNINA9910304132703321"]
[null,null,"Autore",null,"Kaeding Matthias"]
[null,null,"Titolo",null,"Bayesian Analysis of Failure Time Data Using P-Splines / / by Matthias Kaeding"]
[null,null,"Pubbl/distr/stampa",null,"Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Spektrum, , 2015"]
[null,null,"ISBN",null,"3-658-08393-X"]
[null,null,"Edizione",null,"[1st ed. 2015.]"]
[null,null,"Descrizione fisica",null,"1 online resource (117 p.)"]
[null,null,"Collana",null,"BestMasters, , 2625-3615"]
[null,null,"Disciplina",null,"510"]
[{"$nulls":4},"519.2"]
[{"$nulls":4},"570285"]
[{"$nulls":4},"610724"]
[null,null,"Soggetti",null,"Probabilities"]
[{"$nulls":4},"Medicine - Research"]
[{"$nulls":4},"Biology - Research"]
[{"$nulls":4},"Bioinformatics"]
[{"$nulls":4},"Probability Theory"]
[{"$nulls":4},"Biomedical Research"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Relative Risk and Log-Location-Scale Family -- Bayesian P-Splines -- Discrete Time Models -- Continuous Time Models."]
[null,null,null,"Sommario/riassunto","Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions. The MCMC methodology for these models is presented in a unified framework and applied on data sets. Among others, existing algorithms for the grouped Cox and the piecewise exponential model under interval censoring are combined with a data augmentation step for the applications. The author shows that the resulting Gibbs sampler works well for the grouped Cox and is merely adequate for the piecewise exponential model. Contents Relative Risk and Log-Location-Scale Family Bayesian P-Splines Discrete Time Models Continuous Time Models Target Groups Researchers and students in the fields of statistics, engineering, and life sciences Practitioners in the fields of reliability engineering and data analysis involved with lifetimes The "]
[{"$nulls":4},"Author Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics."]
[null,"3.","Record Nr.",null,"UNINA9910810589003321"]
[null,null,"Autore",null,"Tong Hengping <1971->"]
[null,null,"Titolo",null,"Developing econometrics / / Hengqing Tong, T. Krishna Kumar, Yangxin Huang"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, : Wiley, 2011"]
[null,null,"ISBN",null,"9786613306807"]
[{"$nulls":4},"9781119960904"]
[{"$nulls":4},"1119960908"]
[{"$nulls":4},"9781283306805"]
[{"$nulls":4},"1283306808"]
[{"$nulls":4},"9781119954231"]
[{"$nulls":4},"1119954231"]
[{"$nulls":4},"9781119954248"]
[{"$nulls":4},"111995424X"]
[null,null,"Edizione",null,"[2nd ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (487 p.)"]
[null,null,"Disciplina",null,"330.01/5195"]
[null,null,"Soggetti",null,"Econometrics"]
[{"$nulls":4},"Econometric models"]
[{"$nulls":4},"Data mining"]
[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,"Developing Econometrics; Contents; Foreword; Preface; Acknowledgements; 1 Introduction; 1.1 Nature and Scope of Econometrics; 1.1.1 What is Econometrics and Why Study Econometrics?; 1.1.2 Econometrics and Scientific Credibility of Business and Economic Decisions; 1.2 Types of Economic Problems, Types of Data, and Types of Models; 1.2.1 Experimental Data from a Marketing Experiment; 1.2.2 Cross-Section Data: National Sample Survey Data on Consumer Expenditure; 1.2.3 Non-Experimental Data Taken from Secondary Sources: The Case of Pharmaceutical Industry in India"]
[{"$nulls":4},"1.2.4 Loan Default Risk of a Customer and the Problem Facing Decision on a Loan Application1.2.5 Panel Data: Performance of Banks in India by the Type of Ownership after Economic Reforms; 1.2.6 Single Time Series Data: The Bombay Stock Exchange (BSE) Index; 1.2.7 Multiple Time Series Data: Stock Prices in BRIC Countries; 1.3 Pattern Recognition and Exploratory Data Analysis; 1.3.1 Some Basic Issues in "]
[{"$nulls":4},"Econometric Modeling; 1.3.2 Exploratory Data Analysis Using Correlations and Scatter Diagrams: The Relative Importance of Managerial Function and Labor"]
[{"$nulls":4},"1.3.3 Cleaning and Reprocessing Data to Discover Patterns: BSE Index Data1.4 Econometric Modeling: The Roadmap of This Book; 1.4.1 The Econometric Modeling Strategy; 1.4.2 Plan of the Book; Electronic References for Chapter 1; References; 2 Independent Variables in Linear Regression Models; 2.1 Brief Review of Linear Regression; 2.1.1 Brief Review of Univariate Linear Regression; 2.1.2 Brief Review of Multivariate Linear Regression; 2.2 Selection of Independent Variable and Stepwise Regression; 2.2.1 Principles of Selection of Independent Variables; 2.2.2 Stepwise Regression"]
[{"$nulls":4},"2.3 Multivariate Data Transformation and Polynomial Regression2.3.1 Linear Regression after Multivariate Data Transformation; 2.3.2 Polynomial Regression on an Independent Variable; 2.3.3 Multivariable Polynomial Regression; 2.4 Column Multicollinearity in Design Matrix and Ridge Regression; 2.4.1 Effect of Column Multicollinearity of Design Matrix; 2.4.2 Ridge Regression; 2.4.3 Ridge Trace Analysis and Ridge Parameter Selection; 2.4.4 Generalized Ridge Regression; 2.5 Recombination of Independent Variable and Principal Components Regression; 2.5.1 Concept of Principal Components Regression"]
[{"$nulls":4},"2.5.2 Determination of Principal ComponentElectronic References for Chapter 2; References; 3 Alternative Structures of Residual Error in Linear Regression Models; 3.1 Heteroscedasticity: Consequences and Tests for Its Existence; 3.1.1 Consequences of Heteroscedasticity; 3.1.2 Tests for Heteroscedasticity; 3.2 Generalized Linear Model with Covariance Being a Diagonal Matrix; 3.2.1 Diagonal Covariance Matrix and Weighted Least Squares; 3.2.2 Model with Two Unknown Variances; 3.2.3 Multiplicative Heteroscedastic Model; 3.3 Autocorrelation in a Linear Model"]
[{"$nulls":4},"3.3.1 Linear Model with First-Order Residual Autoregression"]
[null,null,null,"Sommario/riassunto","Statistical Theories and Methods with Applications to Economics and Business highlights recent advances in statistical theory and methods that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It provides recently developed computational tools useful for data mining, analysing the reasons to do data mining and the best techniques to use in a given situation.Provides a detailed description of computer algorithms.Provides recently developed computational tools useful for data mi"]