Estimated parameters from the selected joint disease model.

Figshare2017-08-31 更新2026-04-29 收录

关节疾病建模

贝叶斯估计

数据链接：

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Coefficients are summarised with regard to posterior mean and 95% credible interval.

创建时间：

2017-08-31

Identification and Bayesian Estimation of Dynamic Factor Models

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

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

Posterior standard deviation estimates of the 4D SIRD model parameters.

传染病传播模型

贝叶斯估计

Posterior standard deviation estimates of the 4D SIRD model parameters.

NIAID Data Ecosystem20

Identifying Mixtures of Mixtures Using Bayesian Estimation

贝叶斯估计

模型聚类

The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging

DataCite Commons2020-09-04 更新70

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