Parametric Bootstrap

Parametric bootstrapping is a resampling technique used to estimate the uncertainty of statistical estimates and improve the robustness of machine learning models. Current research focuses on extending its application to diverse areas, including time series analysis, high-dimensional data, and neural network training, often incorporating it within algorithms like stochastic gradient descent or deep ensembles. This versatile method enhances the reliability of inference across various fields, from improving confidence intervals in statistical modeling to enabling more accurate uncertainty quantification in complex applications like image reconstruction and automatic speech recognition.