Tutorial overview of simple, stratified, and parametric bootstrapping

Abstract

Students pursuing baccalaureate degrees in electrical engineering and computer engineering are required to take a course in probability and statistics. While the course continues to be mostly conceptual, author started initiatives to introduce data analytics in this course with special emphasis on machine vision applications. Topics such as receiver operating characteristics curves and hypothesis testing are covered through examples and exercises with students having individual datasets. Continuing with this theme, bootstrapping and associated methodologies have now been introduced to facilitate interpretation of machine vision experiments. A demo created that illustrates simple, stratified, and parametric bootstrapping as a means to understand the statistics of a machine vision sensor is presented. It encompasses a number of conceptual topics such as random variables, densities, parameter estimation, chi square testing, etc. alongside data analytics offering a holistic picture of machine learning and machine vision to the undergraduate students.

Introduction of data analytics in the engineering probability course: Implementation and lessons learnt

“…Students were also provided with sample solutions to the data analytics assignments (created using a different data set) to make them aware of the contents of their submission (plots, charts, explanations, etc.). For each assignment, appropriate demos were done in the class [6,9–12].…”

Combining Statistical, Physical, and Historical Evidence to Improve Historical Sea-Surface Temperature Records

“…Here, I supplement earlier estimates by resampling groups with their entire history of diurnal amplitudes and groupwise offsets, which also estimates the path-wise uncertainty. Moreover, to account for the reduced number of groups before the 1950s, a stratified resampling scheme (Shankar, 2020) is used to guarantee that the resampled groups better reflect the prevalence of groups throughout the history of marine observation. Specifically, groups are divided into two strata based on whether they were present in 20-year windows before 1940-1959.…”