Jevgenijs Ivanovs

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In this paperwe consider the first passage process of a spectrally negativeMarkov additive process (MAP). The law of this process is uniquely characterized by a certain matrix function, which plays a crucial role in fluctuation theory. We show how to identify this matrix using the theory of Jordan chains associated with analytic matrix functions. This(More)
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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In this note we identify a simple setup from which one may easily infer various decomposition results for queues with interruptions as well as càdlàg processes with certain secondary jump inputs. In particular, this can be done for processes with stationary or stationary and independent increments. It resulted from an attempt to understand these kind of(More)
In this note we provide a simple alternative derivation of an explicit formula of Kwan and Yang [14] for the probability of ruin in a risk model with a certain dependence between general claim inter-occurrence times and subsequent claim sizes of conditionally exponential type. The approach puts the type of formula in a general context, illustrating the(More)