{"title":"连续概率","authors":"J. Walrand","doi":"10.1002/9781119692430.ch6","DOIUrl":null,"url":null,"abstract":"1. pdf: Pr [X ∈ (x ,x +δ ]] = fX (x)δ . 2. CDF: Pr [X ≤ x ] = FX (x) = ∫ x −∞ fX (y)dy . 3. U[a,b], Expo(λ ), target. 4. Expectation: E [X ] = ∫ ∞ −∞ xfX (x)dx . 5. Expectation of function: E [h(X )] = ∫ ∞ −∞ h(x)fX (x)dx . 6. Variance: var [X ] = E [(X −E [X ])2] = E [X 2]−E [X ]2. 7. Gaussian: N (μ,σ2) : fX (x) = ... “bell curve” Normal Distribution. For any μ and σ , a normal (aka Gaussian) random variable Y , which we write as Y = N (μ,σ2), has pdf","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Continuous Probability\",\"authors\":\"J. Walrand\",\"doi\":\"10.1002/9781119692430.ch6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"1. pdf: Pr [X ∈ (x ,x +δ ]] = fX (x)δ . 2. CDF: Pr [X ≤ x ] = FX (x) = ∫ x −∞ fX (y)dy . 3. U[a,b], Expo(λ ), target. 4. Expectation: E [X ] = ∫ ∞ −∞ xfX (x)dx . 5. Expectation of function: E [h(X )] = ∫ ∞ −∞ h(x)fX (x)dx . 6. Variance: var [X ] = E [(X −E [X ])2] = E [X 2]−E [X ]2. 7. Gaussian: N (μ,σ2) : fX (x) = ... “bell curve” Normal Distribution. For any μ and σ , a normal (aka Gaussian) random variable Y , which we write as Y = N (μ,σ2), has pdf\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2021-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1002/9781119692430.ch6\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1002/9781119692430.ch6","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
1. pdf: Pr [X∈(X, X +δ]] = fX (X)δ。2. CDF: Pr [X≤X] = FX (X) =∫X−∞FX (y)dy。3.U[a,b], Expo(λ),目标。4. 期望:E [X] =∫∞−∞xfX (X)dx。5. 函数期望:E [h(X)] =∫∞−∞h(X)fX (X)dx。6. 方差:var [X] = E (X−E [X]) 2] = E X[2]−E (X) 2。7. 高斯:N (μ,σ2): fX (x) =…“钟形曲线”正态分布。对于任意μ和σ,正态(即高斯)随机变量Y,我们将其写成Y = N (μ,σ2),具有pdf
1. pdf: Pr [X ∈ (x ,x +δ ]] = fX (x)δ . 2. CDF: Pr [X ≤ x ] = FX (x) = ∫ x −∞ fX (y)dy . 3. U[a,b], Expo(λ ), target. 4. Expectation: E [X ] = ∫ ∞ −∞ xfX (x)dx . 5. Expectation of function: E [h(X )] = ∫ ∞ −∞ h(x)fX (x)dx . 6. Variance: var [X ] = E [(X −E [X ])2] = E [X 2]−E [X ]2. 7. Gaussian: N (μ,σ2) : fX (x) = ... “bell curve” Normal Distribution. For any μ and σ , a normal (aka Gaussian) random variable Y , which we write as Y = N (μ,σ2), has pdf
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.