BAYOMA book datasets

Mendeley Data2024-01-31 更新2024-06-28 收录

模态分析

贝叶斯方法

数据链接：

https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/7EVTXG 数据链接链接失效反馈

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资源简介：

Supplementary data sets used for Au SK (Springer 2017) Bayesian Operational Modal Analysis: Modeling, Algorithms, Uncertainty Laws

创建时间：

2024-01-31

Bayesian Adaptive Tucker Decompositions for Tensor Factorization

张量分解

贝叶斯方法

Tucker tensor decomposition offers a more effective representation for multiway data compared to the widely used PARAFAC model. However, its flexibility brings the challenge of selecting the appropria

Taylor & Francis Group2025-05-22 更新280

sj-tex-4-mdm-10.1177_0272989X231162852 – Supplemental material for Using Bayesian Evidence Synthesis Methods to Incorporate Real-World Evidence in Surrogate Endpoint Evaluation

替代终点评估

贝叶斯方法

Supplemental material, sj-tex-4-mdm-10.1177_0272989X231162852 for Using Bayesian Evidence Synthesis Methods to Incorporate Real-World Evidence in Surrogate Endpoint Evaluation by Lorna Wheaton, Anasta

DataCite Commons2024-08-28 更新60

Replication Data for: "Empirical Bayes Methods for Dynamic Factor Models"

动态因子模型

贝叶斯方法

Koopman, Siem Jan, and Mesters, Geert, (2017) “Empirical Bayes Methods for Dynamic Factor Models.” Review of Economics and Statistics 99:3, 486-498.

NIAID Data Ecosystem40

一种用来估计树木生物量模型系统近似不相关回归的贝叶斯方法

生物量估计

贝叶斯方法

提出并应用贝叶斯方法建立了东北落叶松人工林的两个加性生物量模型系统：一个仅以胸径为预测因子，另一个以胸径和全高为预测因子。我们的结果显示了将贝叶斯方法应用于似乎不相关回归生物量模型的优势，不仅获得了更好的模型拟合和预测，而且为构建和更新树木生物量模型提供了不确定性的评估和评估。

国家林业和草原科学数据中心2022-11-02 更新100

The mode and 90% highest posterior density (in parentheses) of the posterior distributions for the Storz and Beaumont [67] models.

种群遗传学

贝叶斯方法

The Bayes factor (BF) indicates the strength of evidence for a population decline with values greater than 10 representing very strong support. N0 and N1 are the current and ancestral Ne respectively.

NIAID Data Ecosystem50

BAYOMA book datasets

资源简介：

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