Abstract
This advanced chapter discusses topics in probabilities of interest to CBR. It is devoted to readers who deal in their applications with stochastic phenomena. Some basic knowledge about probabilities is required. We discuss that the connections between similarities and probabilities are manifold. There are two directions: Probabilities give rise to adequate similarity measures. First we introduced covariance and correlation measures and the Kullback–Leibler measure. Under certain circumstances, probabilities can be estimated from similarities. More sophisticated measures are measures of concordance. They are discussed from the perspective of risk analysis. On the other hand, under certain conditions similarity measures can lead to probabilities. As a way to extend influence diagrams, Bayesian networks are introduced. They contain background knowledge in the form of conditional probabilities. Process models are often of stochastic character. We also discuss linear prediction.
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Richter, M.M., Weber, R.O. (2013). Probabilities. In: Case-Based Reasoning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40167-1_16
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DOI: https://doi.org/10.1007/978-3-642-40167-1_16
Publisher Name: Springer, Berlin, Heidelberg
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