Explores the balance between model simplicity and precision in system identification for control engineering and machine learning.
Explains the statistical concept of included-variable bias in regression models, challenging the traditional 'omitted-variable bias' framing.
A statistical analysis of multicollinearity in regression models, discussing its impact on coefficient interpretation and prediction.
A technical tutorial using Python and JAX to model and correct for survivorship bias in housing market data during the pandemic.
A data scientist's journey from dogmatic Bayesianism to a pragmatic, 'secular' use of Bayesian tools without requiring belief in the model's literal existence.
Explores valid reasons for using simplified assumptions like 'spherical cows' in statistical modeling and theoretical work.
