標題: Titlebook: Computational Probability; Algorithms and Appli John H. Drew,Diane L. Evans,Lawrence M. Leemis Book 2017Latest edition Springer Internation [打印本頁] 作者: 適婚女孩 時間: 2025-3-21 19:00
書目名稱Computational Probability影響因子(影響力)
書目名稱Computational Probability影響因子(影響力)學科排名
書目名稱Computational Probability網(wǎng)絡公開度
書目名稱Computational Probability網(wǎng)絡公開度學科排名
書目名稱Computational Probability被引頻次
書目名稱Computational Probability被引頻次學科排名
書目名稱Computational Probability年度引用
書目名稱Computational Probability年度引用學科排名
書目名稱Computational Probability讀者反饋
書目名稱Computational Probability讀者反饋學科排名
作者: 眉毛 時間: 2025-3-21 22:00 作者: 催眠 時間: 2025-3-22 02:25
Book 2017Latest editionook examines and presents, in a systematic manner, computational probability methods that encompass data structures and algorithms. The developed techniques address problems that require exact probability calculations, many of which have been considered intractable in the past. The book addresses th作者: 引水渠 時間: 2025-3-22 06:48 作者: 色情 時間: 2025-3-22 11:11
Instrumente des Supply Chain Managements, the development of data structures and algorithms to automate the derivation of existing and new results in probability and statistics. Section?12.3, for example, contains the derivation of the distribution of a well-known test statistic that requires 99,500 carefully crafted integrations.作者: 必死 時間: 2025-3-22 13:40
Future of Supply Chain Managemental.?[.]. The bivariate transformation procedure presented in this chapter handles 1-to-1, .-to-1, and piecewise .-to-1 transformations for both independent and dependent random variables. We also present other procedures that operate on bivariate random variables (e.g., calculating correlation and marginal distributions).作者: 必死 時間: 2025-3-22 20:03
Future of Supply Chain Management behind the . procedure in that the transformation algorithms are more general whereas determining the distribution of the product of two random variables is more specific. Some examples given in the chapter demonstrate the algorithm’s application.作者: 6Applepolish 時間: 2025-3-22 21:54
Strategien des Supply Chain Management,eveloped. Maple computer code is developed to implement the transient queue analysis for many system measures of performance without regard to traffic intensity (i.e., the system may be unstable with traffic intensity greater than one).作者: VICT 時間: 2025-3-23 04:39
Computational Probability the development of data structures and algorithms to automate the derivation of existing and new results in probability and statistics. Section?12.3, for example, contains the derivation of the distribution of a well-known test statistic that requires 99,500 carefully crafted integrations.作者: 切割 時間: 2025-3-23 08:11
Bivariate Transformations of Random Variablesal.?[.]. The bivariate transformation procedure presented in this chapter handles 1-to-1, .-to-1, and piecewise .-to-1 transformations for both independent and dependent random variables. We also present other procedures that operate on bivariate random variables (e.g., calculating correlation and marginal distributions).作者: 移植 時間: 2025-3-23 11:33 作者: BABY 時間: 2025-3-23 14:31
Transient Queueing Analysiseveloped. Maple computer code is developed to implement the transient queue analysis for many system measures of performance without regard to traffic intensity (i.e., the system may be unstable with traffic intensity greater than one).作者: ARC 時間: 2025-3-23 19:26 作者: 雪崩 時間: 2025-3-24 01:42 作者: Climate 時間: 2025-3-24 02:33
Instrumente des Supply Chain Management,This 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: 排斥 時間: 2025-3-24 08:27
Strategien des Supply Chain Management,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: 輕快來事 時間: 2025-3-24 11:50
https://doi.org/10.1007/978-3-8349-9549-0The 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: Concerto 時間: 2025-3-24 17:32 作者: 多骨 時間: 2025-3-24 22:33
Wolf-Rüdiger Bretzke,Michael KlettThis chapter considers Bayesian applications of APPL. Section?14.1 introduces Bayesian statistics and motivates the use of a computer algebra system to derive posterior distributions. Section?14.2 develops algorithms in the case of a single unknown parameter. Section?14.3 develops algorithms in the case of multiple unknown parameters.作者: 怒目而視 時間: 2025-3-25 00:14
Sebastian Kahlmeyer,Jürgen O. LiebertThis chapter contains miscellaneous computational probability applications. Section?. concerns algorithms for calculating the probability distribution of the longest path of a series-parallel stochastic activity network with continuous activity durations.作者: oxidant 時間: 2025-3-25 03:21 作者: 威脅你 時間: 2025-3-25 07:42 作者: Needlework 時間: 2025-3-25 14:24 作者: resistant 時間: 2025-3-25 16:50 作者: antidepressant 時間: 2025-3-25 22:27
Bayesian ApplicationsThis chapter considers Bayesian applications of APPL. Section?14.1 introduces Bayesian statistics and motivates the use of a computer algebra system to derive posterior distributions. Section?14.2 develops algorithms in the case of a single unknown parameter. Section?14.3 develops algorithms in the case of multiple unknown parameters.作者: fatuity 時間: 2025-3-26 03:37
Other ApplicationsThis chapter contains miscellaneous computational probability applications. Section?. concerns algorithms for calculating the probability distribution of the longest path of a series-parallel stochastic activity network with continuous activity durations.作者: Conspiracy 時間: 2025-3-26 05:30
Data Structures and Simple Algorithmsy 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: emission 時間: 2025-3-26 09:06
Transformations of Random Variablesheorem from Casella and Berger [16] for many–to–1 transformations, we consider more general univariate transformations. Specifically, the transformation can range from 1–to–1 to many–to–1 on various subsets of the support of the random variable of interest. We also present an implementation of the theorem in APPL and present four examples.作者: 疲勞 時間: 2025-3-26 14:47
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: 奇思怪想 時間: 2025-3-26 17:03 作者: miscreant 時間: 2025-3-27 00:36
https://doi.org/10.1007/978-3-319-43323-3APPL; Computational Probability; Continuous Random Variables; Discrete Random Variables; Maple; Multicrit作者: Adjourn 時間: 2025-3-27 02:14 作者: 云狀 時間: 2025-3-27 08:44 作者: 天賦 時間: 2025-3-27 13:06 作者: 誤傳 時間: 2025-3-27 15:40 作者: IRS 時間: 2025-3-27 19:24 作者: 免除責任 時間: 2025-3-27 22:47
https://doi.org/10.1007/978-3-8349-9549-0monious and flexible mechanism for modeling the evolution of a time series. Some useful measures of these models (e.g., the autocorrelation function or the spectral density function) are oftentimes tedious to compute by hand, and APPL can help ease the computational burden.作者: 尖 時間: 2025-3-28 05:55
Instrumente des Supply Chain Managements,leasant to solve by hand, but are solvable with computational probability using APPL (A Probability Programming Language). 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?12.3,作者: 他去就結(jié)束 時間: 2025-3-28 08:48 作者: 起波瀾 時間: 2025-3-28 10:49
Oliver Lawrenz,Michael Nenningery 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: CHIP 時間: 2025-3-28 14:43 作者: motivate 時間: 2025-3-28 20:48 作者: 固定某物 時間: 2025-3-29 00:37
Future of Supply Chain Managementmented in the . procedure in APPL. The algorithms behind the . and . procedures from the two previous chapters differ fundamentally from the algorithm behind the . procedure in that the transformation algorithms are more general whereas determining the distribution of the product of two random varia作者: 字的誤用 時間: 2025-3-29 04:30
Projektmanagement komplexer SCM-Projekte,discrete 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: 青石板 時間: 2025-3-29 08:57
https://doi.org/10.1007/978-3-8349-9549-0monious and flexible mechanism for modeling the evolution of a time series. Some useful measures of these models (e.g., the autocorrelation function or the spectral density function) are oftentimes tedious to compute by hand, and APPL can help ease the computational burden.作者: 有幫助 時間: 2025-3-29 12:41 作者: 間接 時間: 2025-3-29 19:27 作者: 模仿 時間: 2025-3-29 21:52
Book 2017Latest editiont of the data structures and algorithms (Chapters 3–6 for continuous random variables and Chapters 7–9 for discrete random variables) used in APPL. The book concludes with Chapters 10–15 introducing a sampling of various applications in the mathematical sciences. This book should appeal to researche作者: 女歌星 時間: 2025-3-30 02:03 作者: finite 時間: 2025-3-30 06:07 作者: 大約冬季 時間: 2025-3-30 11:35 作者: Madrigal 時間: 2025-3-30 13:38 作者: predict 時間: 2025-3-30 17:22
Bivariate Transformations of Random Variables variables with arbitrary distributions. The algorithm from the previous chapter for univariate change-of-variables was originally devised by Glen et al.?[.]. The bivariate transformation procedure presented in this chapter handles 1-to-1, .-to-1, and piecewise .-to-1 transformations for both indepe作者: 單獨 時間: 2025-3-30 22:40 作者: 主講人 時間: 2025-3-31 04:23
