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Posterior summaries and 95%-CIs for each model parameter inferred from data for entire Germany.

NIAID Data Ecosystem2026-03-13 收录

数据链接：

https://figshare.com/articles/dataset/Posterior_summaries_and_95_-CIs_for_each_model_parameter_inferred_from_data_for_the_entire_Germany_/16868526 数据链接链接失效反馈

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Posterior summaries and 95%-CIs for each model parameter inferred from data for entire Germany.

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创建时间：

2021-10-25

系统发育学

贝叶斯推断

mcc contains the Maximum Clade Credibility tree, while posterior_sample contains the 100 random trees from the posterior sample.

NIAID Data Ecosystem80

Posterior probability distributions (in percentages) of all 70 possible sets of the making of eight cups of tea that would all be correctly identified under three prior distribution scenarios: Pr3 represents the uniform prior distribution of three possible ability levels 0.5, 0.75, 1; Pr2 represents the uniform prior distribution of binary ability levels 0.5 or 1; Pr2a represents the uniform prior distribution of binary ability levels 0.5 or 0.75 where 0.5 means a pure guessing; 0.75 means 75% of the time the making of the tea will be correctly identified and 1 means the identification will be 100% correct.

贝叶斯推断

实验设计分析

NIAID Data Ecosystem00

Updating Variational Bayes: Fast Sequential Posterior Inference

贝叶斯推断

序贯后验更新

Variational Bayesian (VB) methods usually produce posterior inference in a time frame considerably smaller than traditional Markov Chain Monte Carlo approaches. Although the VB posterior is an approxi

Monash University Figshare2026-02-11 更新00

MME/CRPS models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"

概率预测评估

不确定性量化

This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select

NIAID Data Ecosystem20

Results for the posterior properties of the SW-PH, SL-PH, SG-PH and SEE-PH models using dataset II.

生存分析模型

贝叶斯推断

Results for the posterior properties of the SW-PH, SL-PH, SG-PH and SEE-PH models using dataset II.

NIAID Data Ecosystem00