  Markov automata (MA) constitute an expressive continuous-time compositional
modelling formalism. They appear as semantic backbones for engineering
frameworks including dynamic fault trees, Generalised Stochastic Petri Nets,
and AADL. Their expressive power has thus far precluded them from effective
analysis by probabilistic (and statistical) model checkers, stochastic game
solvers, or analysis tools for Petri net-like formalisms. This paper presents
the foundations and underlying algorithms for efficient MA modelling, reduction
using static analysis, and most importantly, quantitative analysis. We also
discuss implementation pragmatics of supporting tools and present several case
studies demonstrating feasibility and usability of MA in practice.
