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 austinrochford.com
Bayesian Splines with Heteroskedastic Noise in Python with PyMC3 | Austin Rochford
| | matbesancon.xyz
A Pythonic data science project: Part I | ??
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| | Learning by doing: detecting fraud on bank notes using Python in 3 steps.
| | isaacslavitt.com
Isaac Slavitt: Dirichlet-multinomial model for Skittle proportions
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| | twiecki.io
whilemy_mcmc:gently(samples) - The Best Of Both Worlds: Hierarchical Linear Regression in PyMC
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| | [AI summary] This technical blog post explains the advantages of hierarchical Bayesian modeling over non-hierarchical approaches using a case study of predicting radon levels across different US counties with the PyMC3 library.
| | blog.demofox.org
Monte Carlo Integration Explanation in 1D « The blog at the bottom of the sea
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| Let's say that you have a function $latex y=\sin(x)^2$ and you want to know what the area is under the curve between 0 and pi. We could solve this specific problem by doing some algebra and calculus to get the exact answer analytically (which is $latex \frac{\pi}{2}$), but let's pretend like we can't, or don't...