標題: Titlebook: Computational Probability; Algorithms and Appli John H. Drew,Diane L. Evans,Lawrence M. Leemis Book 20081st edition Springer-Verlag US 2008 [打印本頁] 作者: Reagan 時間: 2025-3-21 18:41
書目名稱Computational Probability影響因子(影響力)
作者: 面包屑 時間: 2025-3-21 23:24
Data Structures and Simple Algorithmsause they are defined with a somewhat simpler data structure than that for discrete random variables. The development described here gives a probabilist the ability to automate the instantiation and processing of continuous random variables—key elements of computational probability.作者: 是貪求 時間: 2025-3-22 04:27 作者: 代理人 時間: 2025-3-22 06:42 作者: conference 時間: 2025-3-22 10:27
Stochastic Simulationtational probability in input modeling. Section 10.3 contains a development of an algorithm to find the distribution of the Kolmogorov—Smirnov goodness of-fit test statistic in the all-parameters-known case.作者: integral 時間: 2025-3-22 15:05
https://doi.org/10.1007/978-0-387-74676-0APPL; Maple; Random variable; Simulation; Survival analysis; Transformation; algorithm; algorithms; calculus作者: integral 時間: 2025-3-22 20:26
Springer-Verlag US 2008作者: 生銹 時間: 2025-3-22 22:40
https://doi.org/10.1007/978-3-8349-3948-7This chapter presents an algorithm for computing the PDF of the sum of two independent discrete random variables, along with an implementation of the algorithm in APPL. Some examples illustrate the utility of this algorithm.作者: 吝嗇性 時間: 2025-3-23 04:01
Zusammenfassende Diskussion und Ausblick,This chapter presents an algorithm for computing the PDF of order statistics drawn from discrete parent populations, along with an implementation of the algorithm in APPL. Several examples illustrate the utility of this algorithm.作者: 主講人 時間: 2025-3-23 06:44
https://doi.org/10.1007/978-3-322-82151-5The remaining chapters contain dozens of computational probability applications using APPL. The applications range in complexity from brief examples to results and algorithms requiring long derivations. This chapter surveys some applications in reliability and the closely-related field of survival analysis.作者: Bernstein-test 時間: 2025-3-23 13:14
Sums of Independent Random VariablesThis chapter presents an algorithm for computing the PDF of the sum of two independent discrete random variables, along with an implementation of the algorithm in APPL. Some examples illustrate the utility of this algorithm.作者: Urgency 時間: 2025-3-23 14:16 作者: farewell 時間: 2025-3-23 20:26 作者: TEM 時間: 2025-3-23 23:46 作者: 使乳化 時間: 2025-3-24 03:40
Dheeraj Kumar,Ravi Kant Singh,Apurba Layekessions, plotting, and programming, just to name a few of the basics. APPL is, simply, a set of supplementary Maple commands and procedures that augments the existing computer algebra system. In effect, APPL takes the capabilities of Maple and turns it into a computer algebra system for computationa作者: Vulvodynia 時間: 2025-3-24 07:29
Dheeraj Kumar,Ravi Kant Singh,Apurba Layekause they are defined with a somewhat simpler data structure than that for discrete random variables. The development described here gives a probabilist the ability to automate the instantiation and processing of continuous random variables—key elements of computational probability.作者: Yourself 時間: 2025-3-24 10:45 作者: 名義上 時間: 2025-3-24 18:00 作者: 投票 時間: 2025-3-24 22:07
https://doi.org/10.1007/978-3-8349-3948-7discrete random variables. The first section will show that the nature of the support of discrete random variables makes the data structures required much more complicated than for continuous random variables.作者: 提名 時間: 2025-3-25 02:26 作者: 瘙癢 時間: 2025-3-25 04:15
Roles of the SCM Steering Departmention of the longest path of a series—parallel stochastic activity network with continuous activity durations. Section 11.2 concerns the use of APPL in determining whether a continuous random variable obeys Benford’s law. Finally, Section 11.3 contains miscellaneous computational probability applicati作者: DIKE 時間: 2025-3-25 10:18
Computational Probability978-0-387-74676-0Series ISSN 0884-8289 Series E-ISSN 2214-7934 作者: happiness 時間: 2025-3-25 12:18
Dheeraj Kumar,Ravi Kant Singh,Apurba Layekause they are defined with a somewhat simpler data structure than that for discrete random variables. The development described here gives a probabilist the ability to automate the instantiation and processing of continuous random variables—key elements of computational probability.作者: 我說不重要 時間: 2025-3-25 17:09 作者: Infinitesimal 時間: 2025-3-25 22:06
https://doi.org/10.1007/978-3-8349-3948-7discrete random variables. The first section will show that the nature of the support of discrete random variables makes the data structures required much more complicated than for continuous random variables.作者: IST 時間: 2025-3-26 00:57
Overcoming Performance Trade-Offstational probability in input modeling. Section 10.3 contains a development of an algorithm to find the distribution of the Kolmogorov—Smirnov goodness of-fit test statistic in the all-parameters-known case.作者: 慢跑鞋 時間: 2025-3-26 08:09
John H. Drew,Diane L. Evans,Lawrence M. LeemisThis is an expository monograph with a downloadable modeling language, APPL, that will be used across the Applied Sciences domains including OR/MS, Applied Probability, Engineering, Statistics, Econom作者: NIP 時間: 2025-3-26 08:29 作者: gnarled 時間: 2025-3-26 12:59
Computational Probabilityt to solve by hand, but are solvable with computational probability using A Probability Programming Language (APPL). We define the field of . as the development of data structures and algorithms to automate the derivation of existing and new results in probability and statistics. Section 10.3, for e作者: –吃 時間: 2025-3-26 19:46
Maple for APPLessions, plotting, and programming, just to name a few of the basics. APPL is, simply, a set of supplementary Maple commands and procedures that augments the existing computer algebra system. In effect, APPL takes the capabilities of Maple and turns it into a computer algebra system for computationa作者: frenzy 時間: 2025-3-27 00:29
Data Structures and Simple Algorithmsause they are defined with a somewhat simpler data structure than that for discrete random variables. The development described here gives a probabilist the ability to automate the instantiation and processing of continuous random variables—key elements of computational probability.作者: exhilaration 時間: 2025-3-27 03:26 作者: indoctrinate 時間: 2025-3-27 07:20
Products of Random Variablesmented in the Product procedure in APPL. The algorithm behind the Transform procedure from the previous chapter differs fundamentally from the algorithm behind the Product procedure in that the former concerns the transformation of just . random variable and the latter concerns the product of . rand作者: 擁護 時間: 2025-3-27 09:34
Data Structures and Simple Algorithmsdiscrete random variables. The first section will show that the nature of the support of discrete random variables makes the data structures required much more complicated than for continuous random variables.作者: 無能力之人 時間: 2025-3-27 15:53
Stochastic Simulationtational probability in input modeling. Section 10.3 contains a development of an algorithm to find the distribution of the Kolmogorov—Smirnov goodness of-fit test statistic in the all-parameters-known case.作者: 蛙鳴聲 時間: 2025-3-27 21:40
Other Applicationsion of the longest path of a series—parallel stochastic activity network with continuous activity durations. Section 11.2 concerns the use of APPL in determining whether a continuous random variable obeys Benford’s law. Finally, Section 11.3 contains miscellaneous computational probability applicati作者: cocoon 時間: 2025-3-27 23:20 作者: 保留 時間: 2025-3-28 05:59 作者: filicide 時間: 2025-3-28 08:15
Preeti,Supriyo Roy,Kaushik Kumarhm behind the Product procedure in that the former concerns the transformation of just . random variable and the latter concerns the product of . random variables. Some examples demonstrate the algorithm’s application.作者: 棲息地 時間: 2025-3-28 13:37 作者: 偶像 時間: 2025-3-28 18:37
Computational Probabilityevelopment of data structures and algorithms to automate the derivation of existing and new results in probability and statistics. Section 10.3, for example, contains the derivation of the distribution of a well-known test statistic that requires 99500 carefully crafted integrations.作者: DAMN 時間: 2025-3-28 21:07
Products of Random Variableshm behind the Product procedure in that the former concerns the transformation of just . random variable and the latter concerns the product of . random variables. Some examples demonstrate the algorithm’s application.作者: 口音在加重 時間: 2025-3-29 00:53
Other Applicationsdetermining whether a continuous random variable obeys Benford’s law. Finally, Section 11.3 contains miscellaneous computational probability applications that are not covered elsewhere in the monograph.作者: 大吃大喝 時間: 2025-3-29 04:06 作者: delusion 時間: 2025-3-29 07:29
Dheeraj Kumar,Ravi Kant Singh,Apurba Layekf basic numeric computation, then advance to defining variables, symbolic computations, functions, data types, solving equations, calculus and graphing. Then we will discuss the programming features of Maple that facilitate building the APPL language: loops, conditions and procedures.作者: CHASM 時間: 2025-3-29 12:51 作者: BAIT 時間: 2025-3-29 19:29 作者: countenance 時間: 2025-3-29 21:57 作者: Pcos971 時間: 2025-3-30 03:48
