Authors:
Andreas D. Lattner
1
;
Tjorben Bogon
2
and
Ingo J. Timm
3
Affiliations:
1
Goethe University Frankfurt, Germany
;
2
Goethe University Frankfurt and University of Trier, Germany
;
3
University of Trier, Germany
Keyword(s):
Significance estimation, Simulation control, Statistical tests, Machine learning.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Technologies
;
Operational Research
;
Simulation
;
Soft Computing
;
Symbolic Systems
Abstract:
Simulation is widely used in order to evaluate system changes, to perform parameter optimization of systems, or to compare existing alternatives. Assistance systems for simulation studies can support the user by performing monotonous tasks and keeping track of relevant results. In this paper we present an approach to significance estimation in order to estimate, if – and when – statistically significant results are expected for certain investigations. This can be used for controlling simulation runs or providing information to the user for interaction. We introduce two approaches: one for the classification if significance is expected to occur for given samples and another for the prediction of needed replications until significance migh