Posts

I took the Solve It with Code course from answer.ai, and Advent of Code (AoC) was used there as the programming/problem solving exercise. So here I am, solving AoC for the first time. I have no formal formation in computer science, and I’ve always considered my Python skills to be rather mediocre: the code is barely perfunctory, the art is never there. So this is a good chance for me to improve my Python skills. Also, the age of AI has make coding easy, but this only make algorithmic thinking even more important: we are now, at all time, inundated by a sea of code, and we can only sharpen our swimming skills to avoid being drown. ...

intro Bayesian modeling of global trend, local variation, seasonality, and heterogeneity in time series In this post we’ll implement a time series framework based on a flexible smoothed exponential process. The framework can be used to model global trend, local variation, seasonality and other features that are essential for flexible time series modeling. This post also covers several other features under this general framework, notably: modeling over-dispersed observations with Student-t distribution explicit and extensive modeling of the variation to capture data heteroscedasticity adding regression components when necessary Some other general but important features include: extensive usage of visualisation for model criticism prior and posterior simulation for model inspection and a unified prediction framework. Lastly but most importantly, apart from the specific features and techniques, the post gives a general procedure for Bayesian model building and criticism, which should be useful for building any kind of model for any kind of data. ...