Stochastic dynamical systems arise in many scientific fields, such as asset prices in financial markets, neural activity in ...

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 ...

Cellular dynamics are intrinsically noisy, so mechanistic models must incorporate stochasticity if they are to adequately model experimental observations. As well as intrinsic stochasticity in gene ...

Erkyihun S.T., E Zagona, B. Rajagopalan, (2017). “Wavelet and Hidden Markov-Based Stochastic Simulation Methods Comparison on Colorado River Streamflow,” Journal of Hydrologic Engineering 2017, 22(9): ...

This course is compulsory on the MSc in Operations Research & Analytics. This course is not available as an outside option to students on other programmes. The course covers theory including the ...

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 ...

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 ...

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 is where Process Optimization with Simulation emerges as a critical tool, offering a powerful way to model, analyze, and ...