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www.pythoncharts.com | ||
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austinrochford.com
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| | | | | Splines are a powerful tool when modeling nonlinear relationships. This post shows how to include splines in a Bayesian model in Python using pymc3. In addition, we will show how to use a second splin | |
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andrewpwheeler.com
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| | | | | Here are some notes (for myself!) about how to format histograms in python using pandas and matplotlib. The defaults are no doubt ugly, but here are some pointers to simple changes to formatting to make them more presentation ready. First, here are the libraries I am going to be using. import pandas as pd import... | |
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tomaugspurger.net
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| | | | | Welcome back. As a reminder: In part 1 we got dataset with my cycling data from last year merged and stored in an HDF5 store In part 2 we did some cleaning and augmented the cycling data with data from http://forecast.io. You can find the full source code and data at this project's GitHub repo. Today we'll use pandas, seaborn, and matplotlib to do some exploratory data analysis. For fun, we'll make some maps at the end using folium. | |
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twiecki.io
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| | | [AI summary] This blog post discusses hierarchical linear regression in PyMC3, highlighting its advantages over non-hierarchical Bayesian modeling. The author explores how hierarchical models can effectively handle multi-level data by leveraging the 'shrinkage-effect', which improves predictions by borrowing strength from related groups. Using the radon dataset, the post compares individual and hierarchical models, demonstrating that the hierarchical approach provides more accurate and robust estimates, especially in cases with limited data. The key takeaway is that hierarchical models balance individual and group-level insights, offering the best of both worlds in data analysis. | ||
