In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the j...

In this paper, performance evaluation of a cognitive radio network is conducted. The analysis is based on stochastic network calculus. The system is supposed to work in a Time Division Multiple Access (TDMA) mode with fixed slot length. The wireless channel is modeled as a Gilbert-Elliott (GE) fading channel, where the channel quality transits between state ON and state OFF according to a Marko...

Modeling decision-dependent scenario probabilities in stochastic programs is difficult and typically leads to large and highly non-linear MINLPs that are very difficult to solve. In this paper, we develop a new approach to obtain a compact representation of the recourse function using a set of binary decision diagrams (BDDs) that encode a nested cover of the scenario set. The resulting BDDs can...

Applying the mathematical circulation theory of Markov chains, we investigate the synchronized stochastic dynamics of a discrete network model of yeast cell-cycle regulation where stochasticity has been kept rather than being averaged out. By comparing the network dynamics of the stochastic model with its corresponding deterministic network counterpart, we show that the synchronized dynamics ca...

We study the (perfect Bayesian) equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network)....

performance evaluation of universities is an important issue between researchers. classic data envelopment analysis (dea) models with deterministic data have been used by many authors to measure efficiency of universities in different countries. however, dea with stochastic data are, rarely used to measure efficiency of universities. in this paper, input oriented model in stochastic data envelo...

We present here a method for the study of stochastic neurodynamics in the framework of the Neural Network Master Equation proposed by Cowan. We consider a model neural network composed of two{ state neurons subject to simple stochastic kinetics. We introduce a method based on Feynman diagrams to compute the moment generating function of such a network. We show that the method enables us to obta...

An approach to modeling the impact of disturbances in an agricultural production network is presented. A stochastic model and its approximate deterministic model for averages over sample paths of the stochastic system are developed. Simulations, sensitivity and generalized sensitivity analyses are given. Finally, it is shown how diseases may be introduced into the network and corresponding simu...

This paper describes a new modeling tool for the analysis of non-Markovian stochastic Petri nets (SPN). This tool, called Web-SPN, provides a discrete time approximation of the stochastic behavior of the marking process which results in the possibility to analyze a wider class of PN models with prd, prs and pri concurrently enabled generally distributed transitions. An example is used to show t...