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ddarmon.github.io | ||
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minireference.com
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| | | | | [AI summary] The author discusses the need for a revised introductory statistics curriculum, emphasizing the importance of probability distributions, estimators, and sampling methods. They highlight the inclusion of modern statistical techniques like permutation tests and Bayesian statistics, while also addressing ethical considerations and practical applications. The author also recommends various learning resources for readers interested in statistics. | |
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cyclostationary.blog
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| | | | | Our toolkit expands to include basic probability theory. | |
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statsandr.com
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| | | | | Learn how to run multiple and simple linear regression in R, how to interpret the results and how to verify the conditions of application | |
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yang-song.net
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| | | This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... | ||
