Discrete-time Markov chains are widely used to study communities of competing sessile species. Their parameters are transition probabilities between states (species found at points in space), estimated from repeated observations. The proportion of nonzero entries in the transition matrix has been suggested as a measure of the complexity of interspecific interactions. This is not accurate if mor...

Markov chain model is widely applied in many fields, especially the field of prediction. The classical Discrete-time Markov chain(DTMC) is a widely used method for prediction. However, the classical DTMC model has some limitation when the system is complex with uncertain information or state space is not discrete. To address it, a new belief Markov chain model is proposed by combining Dempster-...

Motivated by an original on-line page-ranking algorithm, starting from an arbitrary Markov chain (Cn) on a discrete state space S, a Markov chain (Cn, Mn) on the product space S, the cat and mouse Markov chain, is constructed. The first coordinate of this Markov chain behaves like the original Markov chain and the second component changes only when both coordinates are equal. The asymptotic pro...

In this paper, we present an approach to improve the efficiency of stochastic simulation for large and dense networks. We are using stochastic Petri nets as modelling framework. The underlying continuoustime Markov chain (CTMC) is converted to an equivalent discrete-time Markov chain (DTMC), this itself gains no efficiency. We improve the efficiency via discrete-time leaps, even though this res...

Many small-scale wireless networks, such as ad hoc networks, operate in a slotted mode with constant packet sizes. The transreceivers have only modest hardware and hence, queuing performance is an important issue. Performance analysis of slotted systems requires more than simply obtaining the performance figures of discrete-time Markov chains. This paper elucidates the importance of characteriz...