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  • Book
  • © 2018

Independent Random Sampling Methods

  • Presents an up-to-date survey of random sampling methods

  • Offers useful methods for practitioners together with detailed analyses for researchers in computational statistics

  • Covers random sampling from multivariate distributions

  • Features a wealth of examples with accompanying computer code

Part of the book series: Statistics and Computing (SCO)

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eBook USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-72634-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
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  • Tax calculation will be finalised during checkout
Softcover Book USD 159.99
Price excludes VAT (USA)
Hardcover Book USD 159.99
Price excludes VAT (USA)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xii
  2. Introduction

    • Luca Martino, David Luengo, Joaquín Míguez
    Pages 1-26
  3. Direct Methods

    • Luca Martino, David Luengo, Joaquín Míguez
    Pages 27-63
  4. Accept–Reject Methods

    • Luca Martino, David Luengo, Joaquín Míguez
    Pages 65-113
  5. Adaptive Rejection Sampling Methods

    • Luca Martino, David Luengo, Joaquín Míguez
    Pages 115-157
  6. Ratio of Uniforms

    • Luca Martino, David Luengo, Joaquín Míguez
    Pages 159-196
  7. Independent Sampling for Multivariate Densities

    • Luca Martino, David Luengo, Joaquín Míguez
    Pages 197-247
  8. Asymptotically Independent Samplers

    • Luca Martino, David Luengo, Joaquín Míguez
    Pages 249-266
  9. Summary and Outlook

    • Luca Martino, David Luengo, Joaquín Míguez
    Pages 267-270
  10. Back Matter

    Pages 271-280

About this book

This book systematically addresses the design and analysis of efficient techniques for independent random sampling. Both general-purpose approaches, which can be used to generate samples from arbitrary probability distributions, and tailored techniques, designed to efficiently address common real-world practical problems, are introduced and discussed in detail. In turn, the monograph presents fundamental results and methodologies in the field, elaborating and developing them into the latest techniques. The theory and methods are illustrated with a varied collection of examples, which are discussed in detail in the text and supplemented with ready-to-run computer code.

The main problem addressed in the book is how to generate independent random samples from an arbitrary probability distribution with the weakest possible constraints or assumptions in a form suitable for practical implementation. The authors review the fundamental results and methods in the field, address the latest methods, and emphasize the links and interplay between ostensibly diverse techniques.


Keywords

  • Random sampling
  • Rejection samplers
  • Ratio-of-uniforms
  • Multidimensional random sampling
  • Markov chain Monte Carlo
  • Independent sampling
  • Accept-reject methods
  • Adaptive rejection sampling
  • Ratio of uniforms
  • quantitative finance

Reviews

“The book contains more than 300 references and has more than 50 coloured figures for better understanding. The 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)

Authors and Affiliations

  • Department of Signal Theory and Communications, Carlos III University of Madrid, Madrid, Spain

    Luca Martino, David Luengo

  • Department of Signal Theory and Communications, Technical University of Madrid, Madrid, Spain

    Joaquín Míguez

About the authors

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.

Bibliographic Information

Buying options

eBook USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-72634-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 159.99
Price excludes VAT (USA)
Hardcover Book USD 159.99
Price excludes VAT (USA)