Glass D.H. (2002) Coherence, Explanation, and Bayesian Networks. In: O’Neill M., Sutcliffe R.F.E., Ryan C., Eaton M., Griffith N.J.L. (eds) Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science, vol 2464. Springer, Berlin, Heidelberg
This paper discusses the relevance of coherence to deciding between competing explanations. It provides a basic definition of coherence in probabilistic terms, which yields a coherence measure and can easily be extended from the coherence of two beliefs to the coherence of n beliefs. Using this definition, the coherence of a set of beliefs can be obtained by making simple extensions to a Bayesian network. The basic definition suggests a strategy for revising beliefs since a decision to reject a belief can be based on maximising the coherence of the remaining beliefs. It is also argued that coherence can provide a suitable approach for inference to the best explanation.