Base case probabilities and sources.

Figshare2015-12-02 更新2026-04-29 收录

贝叶斯估计

概率建模

数据链接：

https://figshare.com/articles/dataset/_Base_case_probabilities_and_sources_/291446 数据链接链接失效反馈

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± Beta(n, N). Beta distribution was used for other probability parameter estimates not shown in this table.

创建时间：

2015-12-02

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

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

Additional file 1: of Comparing lethal dose ratios using probit regression with arbitrary slopes

毒理学剂量分析

概率建模

Calculation of LDs and their ratios.xlsx (344Â kb), which requires Microsoft Excel 2010 or higher. It is available via a link: https://figshare.com/s/f94393f752fcc15faea7 . (XLSX 1493 kb)

Figshare2018-10-06 更新80

ToPS: A framework to manipulate probabilistic models of sequence data

概率建模

ToPS: A framework to manipulate probabilistic models of sequence data

Figshare2016-01-18 更新50