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fharrell.com | ||
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hbiostat.org
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| | | | | Continuous learning from data and computation of probabilities that are directly applicable to decision making in the face of uncertainty are hallmarks of the Bayesian approach. Bayesian sequential designs are the simplest of flexible designs, and continuous learning capitalizes on their efficiency, resulting in lower expected sample sizes until sufficient evidence is accrued due to the ability to take unlimited data looks. Classical null hypothesis testing only provides evidence against the supposition that a treatment has exactly zero effect, and it requires one to deal with complexities if not doing the analysis at a single fixed time. Bayesian posterior probabilities, on the other hand, can be computed at any point in the trial and provide current eviden... | |
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www.statsblogs.com
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| | | | | [AI summary] The article argues against teaching null hypothesis significance testing (NHST) in statistics courses due to its ineffectiveness, danger, and lack of purpose, advocating instead for estimation and meta-analysis as more informative methods. | |
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errorstatistics.com
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| | | | | Below is an email exchange that Andrew Gelman posted on this day 5 years ago on his blog, Statistical Modeling, Causal Inference, and Social Science. (You can find the original exchange, with its 130 comments, here.) Note: "Me" refers to Gelman. I will share my current reflections in the comments. Exchange with Deborah Mayo on... | |
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jxmo.io
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| | | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents. | ||
