Formal error bounds for the state space reduction of Markov chains

We study the approximation of a Markov chain on a reduced state space, for both discrete- and continuous-time Markov chains. In this context, we extend the existing theory of formal error bounds for the approximated transient distributions. In the discrete-time setting, we bound the stepwise increment of the error, and in the continuous-time setting, we bound the rate at which the error grows. In addition, the same error bounds can also be applied to bound how far an approximated stationary distribution is from stationarity. As a special case, we consider aggregated (or lumped) Markov chains, where the state space reduction is achieved by partitioning the state space into macro states. Subsequently, we compare the error bounds with relevant concepts from the literature, such as exact and ordinary lumpability, as well as deflatability and aggregatability. These concepts provide stricter than necessary conditions for settings in which the aggregation error is zero. We also present possible algorithms for finding suitable aggregations for which the formal error bounds are low, and we analyze first experiments with these algorithms on a range of different models. •Formal error bounds on approximations of transient distributions for Markov chains.•The error bounds are tight and applicable in a wide range of model reduction schemes.•Exact lumpability implies error bounds equal to zero but not vice versa.•Error bounds equal to zero imply weak lumpability but not vice versa.•Experiments on a mix of models from the literature and randomly generated models.