Surjective Independence of Causal Influences for Local Bayesian Network Structures
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918150585450496 |
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| author | Drury, Kieran Barons, Martine J. Smith, Jim Q. |
| author_facet | Drury, Kieran Barons, Martine J. Smith, Jim Q. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Surjective Independence of Causal Influences for Local Bayesian Network Structures Drury, Kieran Barons, Martine J. Smith, Jim Q. Methodology Artificial Intelligence 62H22, 62C99, 68T30, 68T37 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. |
| title | Surjective Independence of Causal Influences for Local Bayesian Network Structures |
| topic | Methodology Artificial Intelligence 62H22, 62C99, 68T30, 68T37 |
| url | https://arxiv.org/abs/2509.24759 |