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arXiv · 1901.11483

Perturbed Markov Chains and Information Networks

Benard Abola·Pitos Seleka Biganda·Sergei Silvestrov·Dmitrii Silvestrov·Christopher Engström·John Magero Mango·Godwin Kakuba
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Abstract

The paper is devoted to studies of perturbed Markov chains commonly used for description of information networks. In such models, the matrix of transition probabilities for the corresponding Markov chain is usually regularised by adding a special damping matrix multiplied by a small damping (perturbation) parameter $\varepsilon$. We give effective upper bounds for the rate of approximation for stationary distributions of unperturbed Markov chains by stationary distributions of perturbed Markov chains with regularised matrices of transition probabilities, asymptotic expansions for approximating stationary distributions with respect to damping parameter, as well as explicit upper bounds for the rate of convergence in ergodic theorems for $n$-step transition probabilities in triangular array mode, where perturbation parameter $\varepsilon \to 0$ and $n \to \infty$, simultaneously. The results of numerical experiments are also presented

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Benard Abola, Pitos Seleka Biganda, Sergei Silvestrov, Dmitrii Silvestrov, Christopher Engström, John Magero Mango, Godwin Kakuba. 2019-05-02. Perturbed Markov Chains and Information Networks. https://arxiv.org/abs/1901.11483

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