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 ...

Stochastic modelling of reaction–diffusion systems has emerged as a crucial framework for understanding the complex interplay between chemical reactions and molecular diffusion in biological settings.

Statistics, and real analysis at the undergraduate engineering or mathematics level; graduate level probability and stochastic processes (IEMS 460-1); computer programming in Python; graduate standing ...

This project aims at developing mathematical statistics and probability theory to provide methodologies for modeling and analysis of complex random systems. Statistical methods enable analysis of ...

In this paper we present an exact maximum likelihood treatment for the estimation of a Stochastic Volatility in Mean (SVM) model based on Monte Carlo simulation methods. The SVM model incorporates the ...

A spectral representation-based simulation methodology is proposed to generate sample functions of a multi-variate, multi-dimensional, non-Gaussian stochastic vector field, according to a prescribed ...

We are not currently accepting applications for this course. Register your interest below to be notified when applications open again. These fields often deal with unpredictable phenomena—like ...

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 ...