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.

翻译：贝叶斯网络的强大表达能力因其常需建模大量关系而带来新的挑战。在许多领域中，利用专家判断来补充可用数据变得至关重要。这进而引发两个关键问题：专家判断的认知负担通常极高，且构建完整概率模型需要大量判断。通过在网络局部结构中引入因果影响独立性等假设来限制模型参数空间，可以缓解这两个问题。然而，因果影响独立性假设往往缺乏依据且过于严格。本文提出因果影响满射独立性模型，该模型放宽了因果影响独立性假设，提供了一种更可行、更实用的局部结构建模方案，有助于实现高效的贝叶斯网络参数化。