This post follows on from looking at Bayesian Linear Regression. Here we look at the ability of the above method to track non-stationary problems where the regression coefficients can vary with time.

In this post I talk about reformulating linear regression in a Bayesian framework. This gives us the notion of epistemic uncertainty which allows us to generate probabilistic model predictions. I formulate a model class which can perform linear regression via Bayes rule updates. We show the results are the same as from the statsmodels library. I will also show some of the benefits of the sequential bayesian approach.

Kedro is a python data science library that helps with: creating reproducible, maintainable and modular data science code

How fast is fitting long AR models using Neural prophet? In this quick test we will fit AR based models with different lags and see how long they take to fit.

In this post I was trying out PyTorch Lightning to see if it’s a library that should be used by default alongside PyTorch. I will create the same nonlinear probabilistic network from before, but this time using Lightning. Hence the first few steps are the same as previously shown.