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We consider the problem of scoring Bayesian Network Classifiers (BNCs) on the basis of the conditional loglikelihood (CLL). Currently, optimization is usually performed in BN parameter space, but for perfect graphs (such as Naive Bayes, TANs and FANs) a mapping to an equivalent Logistic Regression (LR) model is possible, and optimization can be performed in(More)
We study the first passage process of a spectrally-negative Markov additive process (MAP). The focus is on the background Markov chain at the times of the first passage. This process is a Markov chain itself with a transition rate matrix Λ. Assuming time-reversibility we show that all the eigenvalues of Λ are real with algebraic and geometric multiplicities(More)
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