A general model validation and testing tool

@article{Vanslette2019AGM,
  title={A general model validation and testing tool},
  author={Kevin Vanslette and Tony Tohme and Kamal Youcef-Toumi},
  journal={Reliab. Eng. Syst. Saf.},
  year={2019},
  volume={195},
  pages={106684}
}

Figures and Tables from this paper

A generalized Bayesian approach to model calibration

Generalized Bayesian Regression and Model Learning

It is shown that the BVM model learning is capable of representing and combining Bayesian and standard regression techniques in a single framework and generalizing these methods.

GSR: A Generalized Symbolic Regression Approach

This paper presents GSR, a Generalized Symbolic Regression approach, by modifying the conventional SR optimization problem formulation, while keeping the main SR objective intact, and proposes a genetic programming approach with a matrix-based encoding scheme.

Systems Approach to Creating Test Scenarios for Automated Driving Systems

Teaching Humans When To Defer to a Classifier via Examplars

This work presents a novel parameterization of the human's mental model of the AI that applies a nearest neighbor rule in local regions surrounding the teaching examples to derive a near-optimal strategy for selecting a representative teaching set.

Performance Analysis of Logistic Model Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

It can be inferred that the LMT-RF model is a promising model, and the outcome of this study will be useful to planners and scientists in landslide sensitivity studies in similar situations.

Improving Regression Uncertainty Estimation Under Statistical Change

This work proposes and implements a loss function for regression uncertainty estimation based on the Bayesian Validation Metric framework while using ensemble learning, and shows that the proposed method is competitive with existing state-of-the-art methods.

Electronic Snellen Chart with Bluetooth Connection and Smartphone App

An electronic Snellen chart tool, which is considered to be more efficient and practical is made, and the questionnaire results showed that the tool is eligible and proper to use because the installation of the application is easy to do.

References

SHOWING 1-10 OF 43 REFERENCES

Toward a Better Understanding of Model Validation Metrics

Four main types of metrics, namely classical hypothesis testing, Bayes factor, frequentist’s metric, and area metric, are examined to provide a better understanding of the pros and cons of each.

Quantitative model validation techniques: New insights

Algorithm for model validation: Theory and applications

This work proposes to formulate the validation of a given model as an iterative construction process that mimics the often implicit process occurring in the minds of scientists, and offers a formal representation of the progressive build-up of trust in the model.

Computational methods for model reliability assessment

Assessing the Reliability of Computational Models under Uncertainty

This paper investigates the use of the model reliability approach for validating computational models in the presence of both aleatory and epistemic uncertainty and proposed methods are illustrated using a model intended to predict the energy dissipation in a lap joint under impact loading.

Validation of reliability computational models using Bayes networks