There is a well-developed framework for stochastic modelling, including algorithms for fast, approximate simulation of cellular dynamics.

As large-scale discrete-event stochastic simulation becomes a tool that is used routinely for the design and analysis of stochastic systems, the need for input-modeling support with the ability to ...

The Network-Free Stochastic Simulator (NFsim) allows the representation of complex biological systems as rule-based models and facilitates coarse-graining of the reaction mechanisms.

The stochastic simulation of such models is also described. The methods discussed in this paper should open the way for many more tests of the rational expectations hypothesis within macroeconomic ...

Stochastic Analysis & Simulation Simulation research derives new methods for the design, analysis, and optimization of simulation experiments. Research on stochastic models develops and analyzes ...

Abstract Wavelet and hidden Markov-based modeling frameworks were developed to better capture the nonstationarity and non-Gaussian characteristics of streamflow that linear models cannot.

This paper documents the specification of a model that was constructed to assess debt sustainability in emerging market economies. Key features of the model include external and fiscal sectors, which ...

This course provides the skills needed to examine and apply modern statistical and machine learning methods to significant real-world computational issues in finance, risk management, and insurance.

This paper presents and estimates a small open economy dynamic stochastic general-equilibrium model (DSGE) for the Jordanian economy. The model features nominal and real rigidities, imperfect ...