Inverse Transform Sampling: Vladimir Ivanovich Smirnov (mathematician), Random, Probability distribution, Cumulative distribution function, Inverse function - Softcover
Synopsis
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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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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Taschenbuch. Condition: Neu. Inverse Transform Sampling | Vladimir Ivanovich Smirnov (mathematician), Random, Probability distribution, Cumulative distribution function, Inverse function | Frederic P. Miller (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786131753268 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 134698222
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Please note that the content of this book primarily consists of articlesavailable from Wikipedia or other free sources online. Inverse transformsampling, also known as the inverse probability integral transform orinverse transformation method or Smirnov transform, is a method forgenerating sample numbers at random from any probability distributiongiven its cumulative distribution function (cdf). Subject to therestriction that the distribution is continuous, this method isgenerally applicable (and can be computationally efficient if the cdfcan be analytically inverted), but may be too computationally expensivein practice for some probability distributions. The Box-Muller transformis an example of an algorithm that is specific to generating samplesfrom a normal distribution, but is more computationally efficient. It isoften the case that, even for simple distributions, the inversetransform sampling method can be improved on: see, for example, theziggurat algorithm and rejection sampling.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch. Seller Inventory # 9786131753268
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