找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Bayesian Inference and Computation in Reliability and Survival Analysis; Yuhlong Lio,Ding-Geng Chen,Tzong-Ru Tsai Book 2022 The Editor(s)

[復(fù)制鏈接]
樓主: Alacrity
51#
發(fā)表于 2025-3-30 10:49:10 | 只看該作者
Bayesian Analysis of a New Bivariate Wiener Degradation Processlifetime have analytic forms and are presented in this chapter. Statistical inference is conducted by data augmentation and Bayesian methods. Weak informative priors are considered, and Gibbs sampling method is utilized to draw sample for the evaluation of the unknown parameters. A simulated example is used for illustration purpose.
52#
發(fā)表于 2025-3-30 15:56:33 | 只看該作者
A Bayesian Approach for the Analysis of Tumorigenicity Data from Sacrificial Experiments Under Weibuthe performance of the developed Bayesian approach with different priors. A comparison is also made with the likelihood estimates determined from an EM algorithm. Finally, a known mice tumor toxicology dataset is analyzed to illustrate the developed Bayesian approach.
53#
發(fā)表于 2025-3-30 19:59:55 | 只看該作者
54#
發(fā)表于 2025-3-30 22:36:13 | 只看該作者
Jasmine Grabher,Madeleine Grawehrs like relapse and a terminal event like death. We develop Bayesian methods to analyze clustered data under the semi-competing risks framework. Subsequently, R program codes are provided to analyze publically available breast cancer data. Parameter estimations are performed based on Gibbs sampling within Metropolis–Hastings algorithm.
55#
發(fā)表于 2025-3-31 03:52:22 | 只看該作者
56#
發(fā)表于 2025-3-31 05:21:23 | 只看該作者
57#
發(fā)表于 2025-3-31 09:49:01 | 只看該作者
Bayesian Analysis for Clustered Data under a Semi-Competing Risks Frameworks like relapse and a terminal event like death. We develop Bayesian methods to analyze clustered data under the semi-competing risks framework. Subsequently, R program codes are provided to analyze publically available breast cancer data. Parameter estimations are performed based on Gibbs sampling within Metropolis–Hastings algorithm.
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-8 02:11
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
靖西县| 宁海县| 甘孜| 当阳市| 桃园县| 略阳县| 哈巴河县| 容城县| 湛江市| 普兰店市| 盐源县| 苍溪县| 辰溪县| 长海县| 汉源县| 大名县| 老河口市| 三台县| 佛山市| 福海县| 修武县| 濮阳县| 祁阳县| 油尖旺区| 四川省| 静宁县| 嘉黎县| 宁蒗| 江西省| 泗水县| 榆林市| 肃北| 淄博市| 鹤山市| 安福县| 桦南县| 安国市| 安丘市| 玉树县| 宝应县| 滦南县|