Efficient descriptor-vector multiplications in stochastic automata networks

Stochastic Automata Networks

Preconditioning for Stochastic Automata Networks

LANGVILLE, AMY N. Preconditioning for Stochastic Automata Networks. (Under the direction of William J. Stewart.) Many very large Markov chains can be modeled efficiently as Stochastic Automata Networks (SANs). A SAN is composed of individual automata that, for the most part, act independently, requiring only infrequent interaction. SANs represent the generator matrix Q of the underlying Markov ...

Stochastic Automata Networks ( SAN ) - Modellingand

This article describes the use of Stochastic Automata Networks (SAN) as a modelling tool. A SAN model consists of some individual stochastic automata that act more or less independently of each other. An automata is represented by a number of states and probabilistic rules that govern the manner in which it moves from one state to the next. The state of a SAN is given by the state of each of it...

Phase-type distributions in stochastic automata networks

Abstract Stochastic Automata Networks (Sans) are high-level formalisms for modeling very large and complex Markov chains in a compact and structured manner. To date, the exponential distribution has been the only distribution used to model the passage of time in the evolution of the different San components. In this paper we show how phase-type distributions may be incorporated into Sans thereb...

Matching Multiplications in Bit-Vector Formulas

Bit-vector formulas arising from hardware verification problems often contain word-level arithmetic operations. Empirical evidence shows that state-of-the-art SMT solvers are not very efficient at reasoning about bit-vector formulas with multiplication. This is particularly true when multiplication operators are decomposed and represented in alternative ways in the formula. We present a pre-pro...