提出更宽松的因果影响独立性模型，降低专家判断负担。
Surjective Independence of Causal Influences for Local Bayesian Network Structures
- 用新模型放松传统因果独立假设，减少参数依赖。
- 在保持建模能力的同时，显著降低所需专家判断数量。
- 适合需要高效构建贝叶斯网络的领域专家和系统设计者。
贝叶斯网络的强大表达能力常因建模关系过多而带来挑战。在许多领域，需结合专家判断补充数据，但这带来双重难题：判断认知负担重，且需大量判断才能建立完整概率模型。通过引入局部结构中的因果影响独立性（ICI）假设可缓解此问题，但该假设往往不成立且过于严格。本文提出广义因果影响独立性（SICI）模型，弱化ICI假设，提供更实用、高效的贝叶斯网络参数化方法，既保持模型灵活性，又大幅降低建模复杂度。
原文摘要 · Abstract (English)
The very expressiveness of Bayesian networks can introduce fresh challenges due to the large number of relationships they often model. In many domains, it is thus often essential to supplement any available data with elicited expert judgements. This in turn leads to two key challenges: the cognitive burden of these judgements is often very high, and there are a very large number of judgements required to obtain a full probability model. We can mitigate both issues by introducing assumptions such as independence of causal influences (ICI) on the local structures throughout the network, restricting the parameter space of the model. However, the assumption of ICI is often unjustified and overly strong. In this paper, we introduce the surjective independence of causal influences (SICI) model which relaxes the ICI assumption and provides a more viable, practical alternative local structure model that facilitates efficient Bayesian network parameterisation.
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