{"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":48724,"journal":{"name":"Law Probability & Risk","volume":"90 1","pages":""},"PeriodicalIF":1.4000,"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\":48724,\"journal\":{\"name\":\"Law Probability & Risk\",\"volume\":\"90 1\",\"pages\":\"\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2021-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Law Probability & Risk\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1002/9781119692430.ch6\",\"RegionNum\":4,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"LAW\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Law Probability & Risk","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1002/9781119692430.ch6","RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"LAW","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:
Law, Probability & Risk is a fully refereed journal which publishes papers dealing with topics on the interface of law and probabilistic reasoning. These are interpreted broadly to include aspects relevant to the interpretation of scientific evidence, the assessment of uncertainty and the assessment of risk. The readership includes academic lawyers, mathematicians, statisticians and social scientists with interests in quantitative reasoning.
The primary objective of the journal is to cover issues in law, which have a scientific element, with an emphasis on statistical and probabilistic issues and the assessment of risk.
Examples of topics which may be covered include communications law, computers and the law, environmental law, law and medicine, regulatory law for science and technology, identification problems (such as DNA but including other materials), sampling issues (drugs, computer pornography, fraud), offender profiling, credit scoring, risk assessment, the role of statistics and probability in drafting legislation, the assessment of competing theories of evidence (possibly with a view to forming an optimal combination of them). In addition, a whole new area is emerging in the application of computers to medicine and other safety-critical areas. New legislation is required to define the responsibility of computer experts who develop software for tackling these safety-critical problems.