根据个体特征建模贝叶斯网络混合模型，实现个性化推断。
Covariate Dependent Mixture of Bayesian Networks
- 用个体特征决定各子群体网络的权重，动态匹配不同人群
- 在青少年心理健康案例中识别出显著影响子群归属的变量
- 适合需要个性化决策的健康与社会政策研究者
从数据中学习贝叶斯网络结构可揭示潜在过程与因果关系，但其有效性依赖于数据群体同质性，而这一条件在现实应用中常被违反。此时使用单一网络结构进行推断可能产生误导，因其无法捕捉子群体差异。为此，我们提出一种新型混合贝叶斯网络建模方法，其中各成分的概率依赖于个体特征。该方法同时识别网络结构与预测子群归属的人口学变量，有助于制定个性化干预策略。我们在模拟实验和青少年心理健康案例研究中评估了该方法，结果表明其在健康、教育及社会政策领域具有提升精准干预的潜力。
原文摘要 · Abstract (English)
Learning the structure of Bayesian networks from data provides insights into underlying processes and the causal relationships that generate the data, but its usefulness depends on the homogeneity of the data population, a condition often violated in real-world applications. In such cases, using a single network structure for inference can be misleading, as it may not capture sub-population differences. To address this, we propose a novel approach of modelling a mixture of Bayesian networks where component probabilities depend on individual characteristics. Our method identifies both network structures and demographic predictors of sub-population membership, aiding personalised interventions. We evaluate our method through simulations and a youth mental health case study, demonstrating its potential to improve tailored interventions in health, education, and social policy.
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