/explore
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ckrapu.github.io | ||
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twiecki.io
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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. | |
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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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austinrochford.com
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| | | | | For my day job, I spend a lot of time thinking about e-commerce analytics and cohort analysis in particular. Statistical age-period-cohort (APC) models are important in many fields such as epidemiolo | |
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sander.ai
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| | | Perspectives on diffusion, or how diffusion models are autoencoders, deep latent variable models, score function predictors, reverse SDE solvers, flow-based models, RNNs, and autoregressive models, all at once! | ||
