Abstract
Historically, it has been claimed that one inference algorithm or technique, say A, is better than another, say B, based on the running times on a test set of Bayesian networks. Recent studies have instead focusing on identifying situations where A is better than B, and vice versa. We review two cases where competing inference algorithms (techniques) have been successfully applied together in unison to exploit the best of both worlds. Next, we look at recent advances in identifying structure and semantics. Finally, we present possible directions of future work in exploiting structure and semantics for faster probabilistic inference.
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Butz, C.J. (2011). Evaluating Probabilistic Inference Techniques: A Question of “When,” not “Which”. In: Benferhat, S., Grant, J. (eds) Scalable Uncertainty Management. SUM 2011. Lecture Notes in Computer Science(), vol 6929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23963-2_4
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DOI: https://doi.org/10.1007/978-3-642-23963-2_4
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