1. Proceedings
  2. I3M
  3. 2019
  4. MAS
  5. 10.46354/i3m.2019.mas.020

Nonparametric frequency polygon estimation for modeling input data

  • a Stephen Hague  , 
  • b Simaan AbouRizk 
  •  
  • ab Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Canada
Cite as
Hague S., AbouRizk S. (2019). Nonparametric frequency polygon estimation for modeling input data. Proceedings of the 18th International Conference on Modelling and Applied Simulation (MAS 2019), pp. 159-165. DOI: https://doi.org/10.46354/i3m.2019.mas.020
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Abstract

To construct valid probability distributions solely from input data, this paper compares three nonparametric density estimators: (1) histograms, (2) Kernel Density Estimation, and (3) Frequency Polygon Estimation. A pseudocode is implemented, a practical example is illustrated, and the Simphony.NET simulation environment is used to fit the nonparametric frequency polygon to a set of data to recreate it as a posterior distribution via the Metropolis-Hastings algorithm.

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