Abstract
First, we saw that Bayesian learning in causal probabilistic networks (and not only here) is twofold discontinuous and therefore may be risky. In applications we have to be aware of that and have to take precautions. Second, we should look out for situations where Bayesian learning is continuous. This will help us also to develop better procedures for the estimation of the Markov kernels of a CPN from data and expert knowledge.
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Oppel, U.G. (1995). Two different types of discontinuity of bayesian learning in causal probabilistic networks. In: Froidevaux, C., Kohlas, J. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1995. Lecture Notes in Computer Science, vol 946. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60112-0_37
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DOI: https://doi.org/10.1007/3-540-60112-0_37
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