Perfect Simulation of Stochastic Automata Networks

The solution of continuous and discrete-time Markovian models is still challenging mainly when we model large complex systems, for example, to obtain performance indexes of parallel and distributed systems. However iterative numerical algorithms, even well-fitted to a multidimensional structured representation of Markov chains, still face the state space explosion problem. Discreteevent simulations can estimate the stationary distribution based on long run trajectories and are also alternative methods to estimate performance indexes of models. Perfect simulation algorithms directly build steady-state samples avoiding the warm-up period and the initial state bias of forward simulations. This paper introduces the concepts of backward coupling and the advantages of monotonicity properties and component-wise characteristics to simulate Stochastic Automata Networks (SAN). The main contribution is a novel technique to solve SAN descriptions originally unsolvable by iterative methods due to l...

• Type: Conference
• Year: 2008
• Where: ASMTA
• Authors: Paulo Fernandes, Jean-Marc Vincent, Thais Webber
• Keywords: ASMTA 2008, Iterative Numerical Algorithms, Mathematics, Performance Indexes, State Space