Distribution Fitting 1. Parameters Estimation under Assumption of Agreement between Observation and Model

TL;DR

This paper reviews methods for estimating distribution parameters assuming the model and observations agree, demonstrating the application on experimental data with various estimation techniques for a Gauss-Laplace distribution.

Contribution

It provides a comparative analysis of different parameter estimation methods under the assumption of model-data agreement for the Gauss-Laplace distribution.

Findings

Different estimation methods yield comparable parameters

Results highlight the effectiveness of specific estimation techniques

Discussion on the suitability of methods for experimental data

Abstract

The methods for parameter estimation under assumption of agreement between observation and model are reviewed. The distribution parameters are obtained for one set of experimental data by using different estimation methods under assumption of Gauss-Laplace theoretical distribution. The results are presented and discussed.