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if we compute the probabilities by maximum likelihood estimation. Clearly, there are no exact (conditional) independences and thus Algorithm 7.3.8 yields a complete graph. However, if we allow for some deviation from an exact conditional independence in order to cope with random fluctuations, we may decide to consider two attributes as (conditionally) independent if their (conditional) mutual information is less than 0.01 bits. If we do so, we obtain A C | B and B C | A. Consequently, Algorithm 7.3.8 will remove the edge (A, C) as well as the edge (B, C), that is, the edges indicated by dotted lines in Figure 7.17. Unfortunately, the resulting graph not only represents the above two conditional independences, but also A C | and B C | , neither of which hold, at least according to the chosen criterion. Note that the algorithm fails, because the induced set of conditional independence statements is inconsistent: the distribution underlying the database shown in Figure 7.17 must be strictly positive, because all value combinations occur. A strictly positive probability distribution satisfies the graphoid axioms (cf. Theorem 4.1.2 on page 95), which comprise the intersection axiom rdlc pdf 417 PDF417 Barcode Creating Library for RDLC Reports | Generate ...
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