The Importance of Bias Terms for Error Bounds and Comparison Results

Bias terms of Markov reward structures are shown to play a key-role to conclude error bound or monotonicity results for steady state measures when dealing with approximate systems such as due to . perturbations . finite truncations . system modifications, or . system comparisons (bounds) The results are illustrated by a non-product form queueing network example with practical phenomena as blocking, overflow and breakdowns. Monotonicity results and explicit error bounds are hereby established for different approximations.

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