Inverse Transform Sampling: Vladimir Ivanovich Smirnov (mathematician), Random, Probability distribution, Cumulative distribution function, Inverse function - Softcover

 
9786131753268: Inverse Transform Sampling: Vladimir Ivanovich Smirnov (mathematician), Random, Probability distribution, Cumulative distribution function, Inverse function
Softcover
ISBN 10: 6131753261 ISBN 13: 9786131753268
Verlag: OmniScriptum, 2010

Inhaltsangabe

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Inverse transform sampling, also known as the inverse probability integral transform or inverse transformation method or Smirnov transform, is a method for generating sample numbers at random from any probability distribution given its cumulative distribution function (cdf). Subject to the restriction that the distribution is continuous, this method is generally applicable (and can be computationally efficient if the cdf can be analytically inverted), but may be too computationally expensive in practice for some probability distributions. The Box-Muller transform is an example of an algorithm that is specific to generating samples from a normal distribution, but is more computationally efficient. It is often the case that, even for simple distributions, the inverse transform sampling method can be improved on: see, for example, the ziggurat algorithm and rejection sampling.

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Reseña del editor

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Inverse transform sampling, also known as the inverse probability integral transform or inverse transformation method or Smirnov transform, is a method for generating sample numbers at random from any probability distribution given its cumulative distribution function (cdf). Subject to the restriction that the distribution is continuous, this method is generally applicable (and can be computationally efficient if the cdf can be analytically inverted), but may be too computationally expensive in practice for some probability distributions. The Box-Muller transform is an example of an algorithm that is specific to generating samples from a normal distribution, but is more computationally efficient. It is often the case that, even for simple distributions, the inverse transform sampling method can be improved on: see, for example, the ziggurat algorithm and rejection sampling.

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Verlag
OmniScriptum
Erscheinungsdatum
2010
Sprache
Englisch
ISBN 10 
6131753261
ISBN 13 
9786131753268
Einband
Tapa blanda
Anzahl der Seiten
76
Herausgeber
Miller Frederic P., Vandome Agnes F., McBrewster John
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
Nicht verfügbar