標(biāo)題: Titlebook: Bayesian Reliability; Michael S. Hamada,Alyson G. Wilson,Harry F. Martz Book 2008 Springer-Verlag New York 2008 Assurance testing.Bayesian [打印本頁] 作者: KEN 時間: 2025-3-21 18:16
書目名稱Bayesian Reliability影響因子(影響力)
書目名稱Bayesian Reliability影響因子(影響力)學(xué)科排名
書目名稱Bayesian Reliability網(wǎng)絡(luò)公開度
書目名稱Bayesian Reliability網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Bayesian Reliability被引頻次
書目名稱Bayesian Reliability被引頻次學(xué)科排名
書目名稱Bayesian Reliability年度引用
書目名稱Bayesian Reliability年度引用學(xué)科排名
書目名稱Bayesian Reliability讀者反饋
書目名稱Bayesian Reliability讀者反饋學(xué)科排名
作者: Impugn 時間: 2025-3-22 00:07 作者: 規(guī)章 時間: 2025-3-22 02:19
Michael S. Hamada,Alyson G. Wilson,Harry F. MartzIncludes supplementary material: 作者: 無能的人 時間: 2025-3-22 05:32 作者: Macronutrients 時間: 2025-3-22 10:24 作者: 藥物 時間: 2025-3-22 13:51
Growth as a Target-Seeking FunctionThis chapter extends the models for component data to systems. This extension requires us to specify logical relationships between the components in a system and how the functioning of the complete system depends on the functioning (or not) of each of its components. We consider models for both independent and dependent component failures.作者: MOT 時間: 2025-3-22 18:17 作者: 開始發(fā)作 時間: 2025-3-23 00:08
System Reliability,This chapter extends the models for component data to systems. This extension requires us to specify logical relationships between the components in a system and how the functioning of the complete system depends on the functioning (or not) of each of its components. We consider models for both independent and dependent component failures.作者: 傾聽 時間: 2025-3-23 02:25
Bayesian Inference, distributions, posterior distributions, and the relation between the three quantities as specified through Bayes’ Theorem. We also provide examples of inference in both discrete and continuous settings.作者: arthroplasty 時間: 2025-3-23 08:10 作者: 紅潤 時間: 2025-3-23 12:03
Regression Models in Reliability,This chapter shows how to incorporate covariates in the analysis of binomial success/failure data, Poisson count data, and lifetime data. Covariates allow us to compare the reliability between two or more different situations. We also discuss how covariates arise in accelerated life testing and in experiments to improve reliability.作者: 高爾夫 時間: 2025-3-23 17:02 作者: 刻苦讀書 時間: 2025-3-23 19:29
Growth of Muscle Tissue and Muscle Mass, distributions, posterior distributions, and the relation between the three quantities as specified through Bayes’ Theorem. We also provide examples of inference in both discrete and continuous settings.作者: 別炫耀 時間: 2025-3-23 23:39 作者: HUMID 時間: 2025-3-24 02:48 作者: 描述 時間: 2025-3-24 08:28
Nutrition and Growth in Infancyng a system to a brand new state to restoring it to the reliability just before the system last failed. Several models for failure count and failure time data collected on repairable systems allow for different degrees of repair effectiveness. The models considered include renewal processes, homogen作者: 紅潤 時間: 2025-3-24 13:05 作者: 連鎖,連串 時間: 2025-3-24 15:36
Sexual Differentiation of the Braine purpose in the 1990s. Assessing reliability with degradation data has a number of advantages. The analyst does not have to wait for failures to occur and can use less acceleration to collect degradation data. This chapter explains how to assess reliability using degradation data and also discusses作者: TAG 時間: 2025-3-24 19:54 作者: 魔鬼在游行 時間: 2025-3-25 02:44 作者: outset 時間: 2025-3-25 06:17 作者: hazard 時間: 2025-3-25 09:23 作者: Spinous-Process 時間: 2025-3-25 12:53 作者: HILAR 時間: 2025-3-25 17:56
Nutrition, Mental Development and LearningThis chapter shows how to incorporate covariates in the analysis of binomial success/failure data, Poisson count data, and lifetime data. Covariates allow us to compare the reliability between two or more different situations. We also discuss how covariates arise in accelerated life testing and in experiments to improve reliability.作者: Banquet 時間: 2025-3-25 21:26
Growth Hormone and Osteoporosiseds a specified requirement at a desired level of confidence. Within a Bayesian hierarchical framework, this chapter determines test plans for binomial, Poisson, and Weibull testing. Also, we develop Weibull assurance test plans using available data from an associated accelerated life testing program.作者: ANTIC 時間: 2025-3-26 03:59 作者: Extricate 時間: 2025-3-26 04:38 作者: TAP 時間: 2025-3-26 08:46
0172-7397 n perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods...The author作者: arthrodesis 時間: 2025-3-26 16:06 作者: 貪婪性 時間: 2025-3-26 19:38
Advanced Bayesian Modeling and Computational Methods,ithms that can be used to generate samples from intractable posterior distributions. These samples provide the basis for subsequent model inference. We also discuss empirical Bayes’ methods. Finally, we describe techniques for assessing the sensitivity of model inferences to prior assumptions and a broadly applicable model diagnostic.作者: 重力 時間: 2025-3-26 23:24
Nutrition and Growth in Infancyeous and nonhomogeneous Poisson processes, modulated power law processes, and a piecewise exponential model. This chapter also addresses how well these models fit the data and evaluates current reliability and other performance criteria, which characterize the reliability of repairable systems.作者: 表狀態(tài) 時間: 2025-3-27 05:04 作者: Brittle 時間: 2025-3-27 08:40
E. Tronick,H. Als,T. B. Brazeltonanning variable situations, we show how to use a genetic algorithm to find a near-optimal plan. This chapter illustrates data collection planning for a number of problems involving binomial, lifetime, accelerated life test, degradation, and system reliability data.作者: 流動性 時間: 2025-3-27 13:32 作者: Mechanics 時間: 2025-3-27 14:50 作者: 安慰 時間: 2025-3-27 19:55
Using Degradation Data to Assess Reliability, how to accommodate covariates such as acceleration factors that speed up degradation and experimental factors that impact reliability in reliability improvement experiments. We also consider situations in which degradation measurements are destructive and conclude by introducing alternative stochastic models for degradation data.作者: 現(xiàn)存 時間: 2025-3-27 22:15
Planning for Reliability Data Collection,anning variable situations, we show how to use a genetic algorithm to find a near-optimal plan. This chapter illustrates data collection planning for a number of problems involving binomial, lifetime, accelerated life test, degradation, and system reliability data.作者: 航海太平洋 時間: 2025-3-28 05:26 作者: THE 時間: 2025-3-28 09:14
Advanced Bayesian Modeling and Computational Methods,istributions that result from these more complicated models in closed form, we begin this chapter with a description of Markov chain Monte Carlo algorithms that can be used to generate samples from intractable posterior distributions. These samples provide the basis for subsequent model inference. W作者: 消極詞匯 時間: 2025-3-28 12:37 作者: Assemble 時間: 2025-3-28 16:18 作者: Etching 時間: 2025-3-28 21:23
