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 sebastianraschka.com
Model evaluation, model selection, and algorithm selection in machine learning
| | magazine.sebastianraschka.com
Tips for LLM Pretraining and Evaluating Reward Models
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| | I'm an LLM Research Engineer with over a decade of experience in artificial intelligence. My work bridges academia and industry, with roles including senior staff at an AI company and a statistics professor. My expertise lies in LLM research and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations.
| | www.huber.embl.de
Modern Statistics for Modern Biology - 4 Mixture Models
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| | If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book.
| | erikbern.com
The hacker's guide to uncertainty estimates ยท Erik Bernhardsson
4.0 parsecs away

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| | I made a New Year's resolution: every plot I make during 2018 will contain uncertainty estimates. Nine months in and I have learned a lot, so I put together a summary of some of the most useful methods.
| | indrajeetpatil.github.io
ggplot2 Based Plots with Statistical Details ggstatsplot
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| Extension of ggplot2, ggstatsplot creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) .