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Gibbs Sampling: Mathematics, Physics, Algorithm, Joint probability, Random variable, Integral, Metropolis-Hastings algorithm, Markov chain Monte Carlo - Softcover

 
9786132669841: Gibbs Sampling: Mathematics, Physics, Algorithm, Joint probability, Random variable, Integral, Metropolis-Hastings algorithm, Markov chain Monte Carlo
Softcover
ISBN 10: 6132669841 ISBN 13: 9786132669841
Verlag: OmniScriptum, 2010

Inhaltsangabe

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In mathematics and physics, Gibbs sampling or Gibbs sampler is an algorithm to generate a sequence of samples from the joint probability distribution of two or more random variables. The purpose of such a sequence is to approximate the joint distribution, or to compute an integral. Gibbs sampling is a special case of the Metropolis-Hastings algorithm, and thus an example of a Markov chain Monte Carlo algorithm. The algorithm is named after the physicist J. W. Gibbs, in reference to an analogy between the sampling algorithm and statistical physics. The algorithm was described by brothers Stuart and Donald Geman in 1984, some eight decades after the passing of Gibbs. Gibbs sampling is applicable when the joint distribution is not known explicitly, but the conditional distribution of each variable is known. The Gibbs sampling algorithm generates an instance from the distribution of each variable in turn, conditional on the current values of the other variables. It can be shown that the sequence of samples constitutes a Markov chain, and the stationary distribution of that Markov chain is just the sought-after joint distribution.

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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. In mathematics and physics, Gibbs sampling or Gibbs sampler is an algorithm to generate a sequence of samples from the joint probability distribution of two or more random variables. The purpose of such a sequence is to approximate the joint distribution, or to compute an integral. Gibbs sampling is a special case of the Metropolis-Hastings algorithm, and thus an example of a Markov chain Monte Carlo algorithm. The algorithm is named after the physicist J. W. Gibbs, in reference to an analogy between the sampling algorithm and statistical physics. The algorithm was described by brothers Stuart and Donald Geman in 1984, some eight decades after the passing of Gibbs. Gibbs sampling is applicable when the joint distribution is not known explicitly, but the conditional distribution of each variable is known. The Gibbs sampling algorithm generates an instance from the distribution of each variable in turn, conditional on the current values of the other variables. It can be shown that the sequence of samples constitutes a Markov chain, and the stationary distribution of that Markov chain is just the sought-after joint distribution.

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Verlag
OmniScriptum
Erscheinungsdatum
2010
Sprache
Englisch
ISBN 10 
6132669841
ISBN 13 
9786132669841
Einband
Tapa blanda
Anzahl der Seiten
148
Herausgeber
Miller Frederic P., Vandome Agnes F., McBrewster John
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
Nicht verfügbar