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austinrochford.com | ||
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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. | |
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www.djmannion.net
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| | | | | Data are sometimes on a circular scale, such as the angle of an oriented stimulus, and the analysis of such data often needs to take this circularity into account. Here, we will look at how we can use PyMC to fit a model to circular data. | |
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dfm.io
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gist.github.com
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| | | Generic `printf` implementation in Idris2. GitHub Gist: instantly share code, notes, and snippets. | ||
