Extending Timed Process Algebra with Discrete Stochastic Time
J. Markovski and E.P. de Vink

When extending timed process algebra with discrete stochastic time,
typical standard notions like time additivity are hard to preserve in
the presence of the race condition. We propose context-sensitive
interpolation as a restricted form of time additivity to accommodate
the extension with stochastic time. We also present a stochastic
process algebra featuring an explicit account of two types of race
conditions in terms of conditional random variables. The approach
enables compositional modeling, a non-trivial expansion law, and
explicit manipulation of maximal progress.