Independent Sampling for Multivariate Densities

  • Luca Martino
  • David Luengo
  • Joaquín Míguez
Part of the Statistics and Computing book series (SCO)


In this chapter, we present several techniques for multivariate independent sampling. We recall some techniques introduced in the previous chapters and show how they can be adapted to a multidimensional setup. Additionally, we provide guidelines for their application in higher dimensional spaces. We also consider the problem of drawing uniformly from a measurable set embedded in \(\mathbb {R}^n\). With this goal, an exhaustive description of transformations of random vectors is given, which extends the study of this approach in the previous chapters.

The problem of sampling a random vector can often be conveniently viewed as generating a (finite) sequence of statistically dependent scalar samples. Thus, in this chapter, we take a slight detour from the main course of the book and show different methods that yield dependent samples, including the use of stochastic processes. Furthermore, a collection of efficient samplers for specific multivariate distributions is described.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Luca Martino
    • 1
  • David Luengo
    • 2
  • Joaquín Míguez
    • 1
  1. 1.Department of Signal Theory and CommunicationsCarlos III University of MadridMadridSpain
  2. 2.Department of Signal Theory and CommunicationsTechnical University of MadridMadridSpain

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