Compact representations of probability distributions in the analysis of superposed GSPNs

Markov chain based analysis of GSPNs suffers from the state space explosion problem. In this paper we combine ideas from two different other approaches to analyze systems with very large state spaces. First, we represent the generator matrix as a stun of Kronecker products of small component matrices. Second, we use an extension of probabilistic decision graphs to represent probability vectors. The combination of these two concepts is the base for an iterative solution technique with the potential to handle extremely large Markov chains resulting from Superposed GSPNs or related model types.