{"title":"两个随机变量的变换","authors":"","doi":"10.1142/9789813202054_0013","DOIUrl":null,"url":null,"abstract":"Problem : (X,Y ) is a bivariate rv. Find the distribution of Z = g(X,Y ). • The very 1st step: specify the support of Z. • X,Y are discrete – straightforward; see Example 0(a)(b) from Transformation of Several Random Variables.pdf. • X,Y are continuous – The CDF approach (the basic, off-the-shelf method) – Special formula (convolution) for Z = X + Y – MGF approach for sums of multiple independent rvs.","PeriodicalId":274238,"journal":{"name":"Probability Models and Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transformations of Two Random Variables\",\"authors\":\"\",\"doi\":\"10.1142/9789813202054_0013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Problem : (X,Y ) is a bivariate rv. Find the distribution of Z = g(X,Y ). • The very 1st step: specify the support of Z. • X,Y are discrete – straightforward; see Example 0(a)(b) from Transformation of Several Random Variables.pdf. • X,Y are continuous – The CDF approach (the basic, off-the-shelf method) – Special formula (convolution) for Z = X + Y – MGF approach for sums of multiple independent rvs.\",\"PeriodicalId\":274238,\"journal\":{\"name\":\"Probability Models and Applications\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Probability Models and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/9789813202054_0013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Probability Models and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/9789813202054_0013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
问题:(X,Y)是一个二元rv。求Z = g(X,Y)的分布。•第一步:指定z的支持•X,Y是离散的-直截了当;参见《若干随机变量的变换》中的例0(a)(b)。•X,Y是连续的- CDF方法(基本的，现成的方法)- Z = X + Y的特殊公式(卷积)-多个独立rvs和的MGF方法。
Problem : (X,Y ) is a bivariate rv. Find the distribution of Z = g(X,Y ). • The very 1st step: specify the support of Z. • X,Y are discrete – straightforward; see Example 0(a)(b) from Transformation of Several Random Variables.pdf. • X,Y are continuous – The CDF approach (the basic, off-the-shelf method) – Special formula (convolution) for Z = X + Y – MGF approach for sums of multiple independent rvs.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
