Abstract. Bayesian Networks are useful for solving a wide range of problems in many domains. Yet, they are exposed to one important challenge when structural and parametrical changes occur. As Bayesian networks lack memory regarding changes over time, there is currently no good way of maintaining a history of changes and their provenance. Thus, any variance in the network’s problem solving behaviour will not be explainable to a user. Within the context of systems that integrate case-based reasoning and Bayesian networks, we suggest to add a casebased reasoning functionality that will retain changes and their provenance, as well as approaches to explain any unexpected problem solving behaviour.
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