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Model parameters and their values.

NIAID Data Ecosystem2026-03-12 收录

贝叶斯估计

不确定性量化

数据链接：

https://figshare.com/articles/dataset/Model_parameters_and_their_values_/13276548 数据链接链接失效反馈

官方服务：

资源简介：

The values in parentheses after the posterior means give the 80% credible interval. See S1 Text for a full description of all prior distributions.

应用场景：

创建时间：

2020-11-23

动态因子模型

贝叶斯估计

We consider a set of minimal identification conditions for dynamic factor models. These conditions have economic interpretations and require fewer restrictions than the static factor framework. Under

DataCite Commons2024-03-24 更新90

METR-LA

流量预测

不确定性量化

Traffic datasets for ICCPS "Uncertainty Quantification for Physics-Informed Traffic Graph Networks"

Figshare2025-02-05 更新60

Mean parameter estimates, standard deviation (SD), Monte Carlo error (MC error), odds ratios (OR), and 95% credible intervals for best-fitting model investigating the effect of covariates on the probability of bovine tuberculosis infection from 2005–2010 in Modified Accredited Zone in Michigan, USA.

牛结核病风险因素分析

贝叶斯估计

“HET” represents the random effect capturing region-wide.

NIAID Data Ecosystem30

S2. Fitting model candidates to high-fidelity D 1j datasets from Anchoring the mean generation time in the SEIR to mitigate biases in ℜ 0 estimates due to uncertainty in the distribution of the epidemiological delays

传染病动力学模型

贝叶斯估计

This appendix illustrates the process of fitting various parameterisations of the SE1IjR model (M1j) to high-fidelity D1j incidence reports. We mean by parameterisation the decision of categorising mo

NIAID Data Ecosystem60

Monte Carlo simulation and correction factors.

MonteCarlo模拟

不确定性量化

Compressed file containing a description of the methodology used to perform the Monte Carlo simulations and raw data from Figs 4 and 5. (ZIP)

Figshare2018-05-15 更新40