to evaluate and predict component-based software security, a two-dimensional model of software security is proposed by stochastic petri net in this paper. in this approach, the software security is modeled by graphical presentation ability of petri nets, and the quantitative prediction is provided by the evaluation capability of stochastic petri net and the computing power of markov chain. each...

This paper develops a theoretical framework based on Markov chains for the simple genetic algorithm (operators of reproduction, crossover, and mutation). We prove the existence of a unique stationary distribution for the Markov chain when mutation probability is used as a control parameter. We also show that there is a stationary distribution limit when the control parameter approaches zero. Fi...

This paper presents a fine-grained parallel genetic algorithm with mutation rate as a control parameter. The function of the mutation rate is similar to the function of temperature parameter in the simulated annealing [Lundy’86, Otten’89, and Romeo’85]. The parallel genetic algorithm presented here is based on a Markov chain [Kemeny’60] model. It has been proved that fine-grained parallel genet...

The Baum-Welch algorithm is a technique for the maximum likelihood parameter estimation of probabilistic functions of Markov processes. We apply this technique to nonstationary Markov processes and explore a relationship between the Baum-Welch algorithm and the BCJR algorithm. Furthermore, we apply the Baum-Welch algorithm to two nonstationary Markov processes and obtain the turbo decoding algo...

We introduce a three-parameter random walk with reinforcement, called the (θ,α,β) scheme, which generalizes the linearly edge reinforced random walk to uncountable spaces. The parameter β smoothly tunes the (θ,α,β) scheme between this edge reinforced random walk and the classical exchangeable two-parameter Hoppe urn scheme, while the parameters α and θ modulate how many states are typically vis...

This paper is concerned with persistent system identification for plants that are equipped with binary sensors whose unknown parameter is a random process represented by a Markov chain. We treat two classes of problems. In the first class, the parameter is a stochastic process modeled by an irreducible and aperiodic Markov chain with transition rates sufficiently faster than adaptation rates of...

We propose a simple technique for verifying probabilistic models whose transition probabilities are parametric. The key is to replace parametric transitions by nondeterministic choices of extremal values. Analysing the resulting parameter-free model using off-the-shelf means yields (refinable) lower and upper bounds on probabilities of regions in the parameter space. The technique outperforms t...

For large vocabulary recognition system, as well as for flexible vocabulary applications using hidden Markov model(HMM), parameter smoothing and tying have been used to increase the reliability of models. This paper describes bottom-up and topdown clustering techniques for state level tying. This paper also describes a method of applying parameter smoothing to the clustered states and covarianc...

In this paper, the two parameter ADK entropy, as a generalized of Re'nyi entropy, is considered and some properties of it, are investigated. We will see that the ADK entropy for continuous random variables is invariant under a location and is not invariant under a scale transformation of the random variable. Furthermore, the joint ADK entropy, conditional ADK entropy, and chain rule of this ent...

575 repeated eigenvalues. For this example, the ARIES software package produces results which are identical with those obtained by the Laplace transform method. The example serves to illustrate the validity of using (2. 2) to solve closed ARIES systems. The solution to the Markov chain presented in Fig. 2 and using the Transform method is given by the following set of expressions: p , (t) = e-(...