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This paper studies quantitative model checking of infinite tree-like (continuous-time) Markov chains. These tree-structured quasi-birth death processes are equivalent to probabilistic pushdown automata and recursive Markov chains and are widely used in the field of performance evaluation. We determine time-bounded reachability probabilities in these processes-which with direct methods, i.e., uniformization, result in an exponential blow-up-by applying abstraction. We contrast abstraction based on Markov decision processes (MDPs) and interval-based abstraction; study various schemes to partition the state space, and empirically show their influence on the accuracy of the obtained reachability probabilities. Results show that grid-like schemes, in contrast to chain- and tree-like ones, yield extremely precise approximations for rather coarse abstractions.