Section: New Results

Stochastic Bounds with a Low Rank Decomposition

In [5] , we investigate how we can bound a discrete time Markov chain (DTMC) by a stochastic matrix with a low rank decomposition. We show how the complexity of the analysis for steady-state and transient distributions can be simplified when we take into account the decomposition. Finally, we show how we can obtain a monotone stochastic upper bound with a low rank decomposition.