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Additional file 1: of Discovering causal interactions using Bayesian network scoring and information gain

NIAID Data Ecosystem2026-03-09 收录

数据链接：

https://figshare.com/articles/dataset/Additional_file_1_of_Discovering_causal_interactions_using_Bayesian_network_scoring_and_information_gain/4428164 数据链接链接失效反馈

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资源简介：

Supplement A. (ZIP 1133 kb)

应用场景：

创建时间：

2016-12-15

因果发现

非高斯模型

One of the established approaches to causal discovery consists of combining directed acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional dependencies of effects on th

DataCite Commons2026-05-21 更新390

Partition MCMC for Inference on Acyclic Digraphs

贝叶斯网络

MCMC算法

Acyclic digraphs are the underlying representation of Bayesian networks, a widely used class of probabilistic graphical models. Learning the underlying graph from data is a way of gaining insights abo

Mendeley Data2024-06-25 更新120

Inference results of Boolean network (Best-Fit with optimal discretization k-means and maxK = 2), Dynamic Bayesian Network (with least squares estimation, and default parameters alpha1 = 0.5 and alpha2 = 0.05 for first order dependencies and full dependencies correspondingly) and Boolean Function Network (maxTimeDelay = 10 and p1 = 0.005).

布尔网络

贝叶斯网络

NIAID Data Ecosystem40

Supplementary data-BNs.xlsx

贝叶斯网络

稀疏模型

Node probability tables for the Bayesian networks models of different SSM approaches.

DataCite Commons2020-08-26 更新270

Supplemental Files.zip

诊断分类

贝叶斯网络

Supplemental files associated with the article entitled "Using Diagnostic Classification Models to Validate Attribute Hierarchies and Evaluate Model Fit in Bayesian Networks"

DataCite Commons2020-08-28 更新60

Additional file 1: of Discovering causal interactions using Bayesian network scoring and information gain

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