Surjective Independence of Causal Influences for Local Bayesian Network Structures
This work addresses the problem of efficient Bayesian network parameterisation for domains relying on expert judgements, offering an incremental improvement over existing local structure models.
The paper tackles the high cognitive burden and large number of expert judgements required for Bayesian network parameterisation by introducing the surjective independence of causal influences (SICI) model, which relaxes the overly strong independence of causal influences (ICI) assumption to provide a more practical alternative.
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.