The stochastic reaction network in which chemical species evolve through a set of reactions is widely used to model processes physics, chemistry and biology. To characterize the evolving joint probability distribution state space counts requires solving system ordinary differential equations, master equation, where size counting increases exponentially with type species. This makes it challengi...

Due to the low complexity in arithmetic unit design, stochastic computing (SC) has attracted considerable interest implement Artificial Neural Networks (ANNs) for resources-limited applications, because ANNs must usually perform a large number of operations. To attain high computation accuracy an SC-based ANN, extended logic is utilized together with standard SC units and thus, divider required...

We introduce a new approach to model the behavior of neuronal signal transduction networks using stochastic differential equations. We present first a mathematical formulation for a stochastic model of protein kinase C pathway. Different kinds of numerical integration methods, including the explicit and implicit Euler–Maruyama methods, are used to solve the Itô form of the stochastic model. Sto...

This paper deals with the fundamental problem of estimating the distribution function (df) of the duration of the longest path in the stochastic activity network such as PERT network. First a technique is introduced to reduce variance in Conditional Monte Carlo Sampling (CMCS). Second, based on this technique a new procedure is developed for CMCS. Third, a combined approach of simulation and ap...