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jaketae.github.io | ||
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matthewmcateer.me
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| | | | | Important mathematical prerequisites for getting into Machine Learning, Deep Learning, or any of the other space | |
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www.randomservices.org
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| | | | | [AI summary] The text covers various topics in probability and statistics, including continuous distributions, empirical density functions, and data analysis. It discusses the uniform distribution, rejection sampling, and the construction of continuous distributions without probability density functions. The text also includes data analysis exercises involving empirical density functions for body weight, body length, and gender-specific body weight. | |
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deepai.org
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| | | | | Bayesian inference refers to the application of Bayes' Theorem in determining the updated probability of a hypothesis given new information. | |
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www.markhw.com
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| | | [AI summary] The blog post discusses modeling variance in data using the gamlss package in R, focusing on the user's film ratings over time. It highlights how the standard deviation of ratings increases with the release year of films, reflecting the user's movie selection habits. The analysis shows that older films have higher average ratings and lower variability, while newer films have lower average ratings and higher variability. The post emphasizes the importance of considering variance in social phenomena and provides practical examples using R for data visualization and statistical modeling. | ||
