Abstract:2 We investigate how we can bound a discrete time Markov chain (DTMC) by a stochastic matrix with a low rank decomposition. In the first part of the article, we show the links with previous results for matrices with a decomposition of size 1 or 2. Then 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.
Abstract. We present the new version of XBorne a software tool for the probabilistic modeling with Markov chains. The tool which has been developed initially as a testbed for the algorithmic stochastic comparisons of stochastic matrices and Markov chains, is now a general purpose framework which can be used for the Markovian modelling in education and research.
Abstract. We present the new version of XBorne a software tool for the probabilistic modeling with Markov chains. The tool which has been developed initially as a testbed for the algorithmic stochastic comparisons of stochastic matrices and Markov chains, is now a general purpose framework which can be used for the Markovian modelling in education and research.
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