IEEE

A Review on Bayesian Methods for Uncertainty Quantification in Machine Learning Models Enhancing Predictive Accuracy and Model Interpretability

Abstract: This research study reviews the statistical fundamentals of machine learning with a focus on Bayesian methods to quantify the uncertainty in model predictions. Bayesian statistics provides a ...
Frontiers

Application of Game-based Learning of Mathematics in Primary School Grade One——Taking a Primary School in Beijing as an Example

The final, formatted version of the article will be published soon. In primary school mathematics teaching, game-based learning can assist teachers in enhancing classroom efficiency, diversifying ...
GitHub

A Library for Uncertainty Quantification

Proper estimation of predictive uncertainty is fundamental in applications that involve critical decisions. Uncertainty can be used to assess reliability of model predictions, trigger human ...
EdSource

San Diego math decline reveals need for statewide strategy

A report about declining math preparation at UC San Diego has been generating hysterical headlines in national news outlets. The steep drops in math performance of incoming students, highlighted in a ...
IEEE

Model-Free Adaptive Control of Discrete-Time Nonlinear Systems with Embedded Multimodel Framework: Approach and Example

Abstract: This article introduces a novel approach that combines a multimodel technique with model-free adaptive control (MFAC) to address the limitations of the full-form dynamic linearization (FFDL) ...