What is it about?
It is about how parameter uncertainty is incorporated in a simulation-based prediction model
Featured Image
Photo by CHUTTERSNAP on Unsplash
Why is it important?
Parameters of a random component in a simulation model are usually estimated from real data so that parameter uncertainty must by assessed in the prediction process.
Perspectives
A Bayesian framework to assess parameter uncertainty of random components of a simulation-based prediction process has many advantages compared to other approaches and I believe it will be adopted for most practicioners as far as they understand its principles.
Dr. David Fernando Muñoz
Instituto Tecnologico Autonomo de Mexico
Read the Original
This page is a summary of: Estimation of Expectations and Variance Components in Two-Level Nested Simulation Experiments, AppliedMath, August 2023, MDPI AG,
DOI: 10.3390/appliedmath3030031.
You can read the full text:
Contributors
The following have contributed to this page
