Positive systems play an important role in and control theory have found many applications multiagent systems, neural networks, biology, more. map the nonnegative orthant to itself (and also nonpositive itself). In other words, they set of vectors with zero sign variations itself. this article, discrete-time linear that up k - 1 are introduced. For special case = these reduce positive systems. ...

We propose a model in which dividend payments occur at regular, deterministic intervals an otherwise continuous model. This contrasts traditional models where either the payment of dividends is controlled or dynamics are given by discrete time processes. Moreover, between two payments, structure allows for other types control; we consider possibility equity issuance any point time. The value ch...

in this article, the discrete time state space model with first-order autoregressive dependent process noise is considered and the recursive method for filtering, prediction and smoothing of the hidden state from the noisy observation is designed. the explicit solution is obtained for the hidden state estimation problem. finally, in a simulation study, the performance of the designed method ...

in this work, we apply the radial basis functions for solving the time fractional diffusion-wave equation defined by caputo sense for                         . the problem is discretized in the time direction based on finite difference scheme and is continuously approximated by using the radial basis functions in the space direction which achieves the semi-discrete solution. numerical results s...

We revisit and extend the original deenition of discrete-time stochastic Petri nets, by allowing the ring times to have a \defective discrete phase distribution". We show that this formalism still corresponds to an underlying discrete-time Markov chain. The structure of the state for this process describes both the marking of the Petri net and the phase of the ring time for of each transition, ...

4 Martingales 35 4.1 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.2 Doob Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.3 Optional Sampling Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.4 Inequalities and Convergence . . . . . . . . . . . ...