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 ...

This is a preview. Log in through your library . Abstract (1) Spatial processes in an acarine predator-prey system were simulated by a stochastic population model. (2) The model describes interactions ...

Abstract: A stochastic modeling methodology for 3D foliage is presented, aimed at enhancing ray-tracing simulations. The model supports adjustable stochastic geometry, density, and shape to capture ...

Simulation research derives new methods for the design, analysis, and optimization of simulation experiments. Research on stochastic models develops and analyzes models of systems with random behavior ...

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 ...

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 ...

Abstract: To tackle the insufficient representation of uncertainty and weak structural adaptability in nonlinear system modeling, a dynamic interval type-2 fuzzy stochastic configuration network ...

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 ...