book chapter · 2000

Algorithms for sampling

Cited by 0Abstract and details only

Abstract

Abstract Monte Carlo methods are an essential part of any practitioner’s toolkit when considering the approximation of integrals. Typically this will require the ability to generate from a wide variety of probability distributions. In fact, a major constraint on the implementation of Monte Carlo methods can be the lack of a suitable algorithm for generation in a given context.

Cite this paper

APA 7

Evans, M. L., & Swartz, T. B. (2000). Algorithms for sampling. https://doi.org/10.1093/oso/9780198502784.003.0003

MLA 9

Evans, Michael L., and Tim B. Swartz. “Algorithms for sampling.” 2000, pp. 30–53, https://doi.org/10.1093/oso/9780198502784.003.0003.

Chicago

Evans, Michael L., and Tim B. Swartz. 2000. “Algorithms for sampling.” https://doi.org/10.1093/oso/9780198502784.003.0003.

Harvard

Evans, M.L. and Swartz, T.B. (2000) ‘Algorithms for sampling’. Available at: https://doi.org/10.1093/oso/9780198502784.003.0003.

BibTeX

@incollection{evans2000algorithms,
  title = {{Algorithms for sampling}},
  author = {Evans, Michael L. and Swartz, Tim B.},
  pages = {30--53},
  year = {2000},
  doi = {10.1093/oso/9780198502784.003.0003}
}

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