Béda révisi "Sebaran seragam"

107 bita dipupus ,  17 tahun yang lalu
Lamun ''u'' ngarupakeun nilai sampel tina standar sebaran seragam , mangka nilai ''a'' + (''b'' - ''a'')''u'' nuturkeun sebaran seragam nu di-parameterisasi ku ''a'' jeung ''b'', saperti nu dijelaskeun di luhur. Transpromasi sejenna bisa digunakeun keur nyaruakeun sebaran statistik sejenna tina sebaran seragam (tempo ''pamakean'' di handap)
 
=== UsesPamakean ofsebaran the uniform distributionseragam ===
 
InDina [[statisticsstatistik]], when alamun [[p-value]] isdipake usedsalaku astes astatistik test statistic for a simplekeur [[null hypothesis]] sederhana, andjeung the distribution of thesebaran test statisticstatistik iskontinyu continuous, thenmangka thetes teststatistik statisticbakal iskasebar uniformlyseragam distributed betweenantara 0 andjeung 1 if thelamun null hypothesis is truebener.
 
Sanajan sebaran seragam teu ilahar kapanggih di alam, sabageanna bisa dipake keur sampling tina sebaran acak.
Although the uniform distribution is not commonly found in nature, it is particularly useful for sampling from arbitrary distributions.
 
AMetoda generalnu methodgeus isilahar thenyaeta [[inverse transform sampling method]], whichnu uses themake [[cumulative distribution function]] (CDF) of thetina target variabel random variable. ThisMetoda method isieu verykacida usefulngabantu indina theoreticalpagawean worktioritis. SinceSaprak simulationssimulasi usingmake thismetoda methodieu requiremerlukeun ''inverting the'' CDF oftina thevariabel target variable, alternative methods have beenmetoda divisedalternatipna forgeus thedibagi caseskeur wherekasus thenumana CDF isteu notdipikanyaho knowndina inbentuk closed formraket. OneSalah suchsahiji methodmetodana isnyaeta [[rejection sampling]].
 
The [[Sebaran normal distribution]] isngarupakeun anconto importantpenting examplemangsa where themetoda ''inverse transform'' methodteu isepisien. notSanajan efficient. Howeverkitu, there iseta anngarupakeun exactmetoda methodeksak, the [[Box-Muller transformation]], whichnu uses themake ''inverse transform'' tokeur convertkonversi two independent uniformdua [[random variable]]s intoseragam twobebas independentka dua [[normalsebaran distribution|normally distributednormal]] random variablesvariabel bebas.
 
[[Category:Probability distributions]]
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