  Given a supervised machine learning problem where the training set has been
subject to a known sampling bias, how can a model be trained to fit the
original dataset? We achieve this through the Bayesian inference framework by
altering the posterior distribution to account for the sampling function. We
then apply this solution to binary logistic regression, and discuss scenarios
where a dataset might be subject to intentional sample bias such as label
imbalance. This technique is widely applicable for statistical inference on big
data, from the medical sciences to image recognition to marketing. Familiarity
with it will give the practitioner tools to improve their inference pipeline
from data collection to model selection.
