developed standard minimum information probability distributions

Name: developed standard minimum information probability distributions
Brand: Sudwestdeutscher Verlag Fur Hochschulschriften AG
SKU: 9783838137605
Price: 2180 CZK
Availability: InStock

Autor: Surath Sen

Jazyk:

Vazba: Brožovaná

Vydavatel: Sudwestdeutscher Verlag Fur Hochschulschriften AG

Dostupnost: Skladem u dodavatele

Odesíláme za 5-8 dnů

2 180 Kč

In a given situation describing a real phenomenon, the random structure of a random variable is defi...

Informace o knize

Autor

Surath Sen

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2014

Stránek

604

EAN

9783838137605

ISBN

3838137604

Enbook ID

02635452

Vydavatel

Sudwestdeutscher Verlag Fur Hochschulschriften AG

Hmotnost

875

Rozměry

152 x 229 x 34

Kompletní popis

In a given situation describing a real phenomenon, the random structure of a random variable is defined by its probability distribution (discrete or continuous), the support of the probability distribution being necessarily bounded (i.e. bounded both above and below). The moments of the random variable, which are predetermined as the parameters of the probability distribution, are comparatively easier to estimate than parameters of any other forms. The predetermined moments of the random variable, being the parameters, has to determine the probability distribution uniquely. This is precisely the case, when a given finite number of (arithmetic) moments determines an exponential polynomial probability distribution uniquely. The true probability distribution of a random variable does exist, but is generally unknown and thereby needs to be estimated by certain statistical means. One of such estimators is the minimum information probability distribution, whose type (constant, monotone, uni-extremal or multi-extremal) must be known in advance by means of empirical observations /experiences of the past. A multi-extremal type is not of standard type from the statistical point of view.

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developed standard minimum information probability distributions

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