Independent Random Sampling Methods by Luca Martino
This book systematically addresses the design and analysis of efficient techniques for independent random sampling.-
Introductory Statistics with R
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R for SAS and SPSS Users
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Mixed-Effects Models in S and S-PLUS
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The Grammar of Graphics
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An Introduction to Statistics with Python
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Developing Statistical Software in Fortran 95
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Software for Data Analysis
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Basic Elements of Computational Statistics
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Modern Applied Statistics with S-PLUS
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Modern Applied Statistics with S
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SAS for Data Analysis
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Visualizing Time
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Evolutionary Statistical Procedures
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XploRe: An Interactive Statistical Computing Environment
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Computer Intensive Methods in Statistics
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Local Regression and Likelihood
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Numerical Analysis for Statisticians
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Numerical Linear Algebra for Applications in Statistics
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Automatic Nonuniform Random Variate Generation
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Elements of Network Science
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Graphics of Large Datasets
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S Programming
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Computational Statistics
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Branch-and-Bound Applications in Combinatorial Data Analysis
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The R Software
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Random Number Generation and Monte Carlo Methods
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Elements of Computational Statistics
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A SAS/IML Companion for Linear Models
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Applied Quantitative Finance
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Visualization and Imputation of Missing Values
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Fundamentals of Supervised Machine Learning
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Variowin
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Numerical Bayesian Methods Applied to Signal Processing
“The book contains more than 300 references and has more than 50 coloured figures for better understandingThe book can be recommended to all readers, who are interested in this field, e.g. ‘engineers working in signal theory or statisticians interested in computational methods or scientists working in the fields of biology, quantitative finance or physics, where complex models that demand Monte Carlo computations are needed’.” (Ludwig Paditz, zbMATH 1414.62007, 2019)
Luca Martino is currently a research fellow at the University of Valencia, Spain, after having held positions at the Carlos III University of Madrid, Spain, the University of Helsinki, Finland and the University of São Paulo, Brazil. His research interests are in the fields of statistical signal processing and computational statistics, especially in connection with Bayesian analysis and Monte Carlo approximation methods.
David Luengo is an Associate Professor at the Technical University of Madrid, Spain. His research interests are in the broad fields of statistical signal processing and machine learning, especially Bayesian learning and inference, Gaussian processes, Monte Carlo algorithms, sparse signal processing and Bayesian non-parametrics. Dr. Luengo has co-authored over 70 research papers, which were published in international journals and conference volumes.
Joaquín Míguez is an Associate Professor at the Carlos III University of Madrid, Spain. His interests are in the fields of applied probability, computational statistics, dynamical systems and the theory and applications of the Monte Carlo methods. Having published extensively and lectured internationally on his research, he was a co-recipient of the IEEE Signal Processing Magazine Best Paper Award in 2007.
| SKU | Unavailable |
| ISBN 13 | 9783030102418 |
| ISBN 10 | 3030102416 |
| Title | Independent Random Sampling Methods |
| Author | Luca Martino |
| Series | Statistics And Computing |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer Nature Switzerland AG |
| Year published | 2019-02-09 |
| Number of pages | 280 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |