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easystats.github.io | ||
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dustintran.com
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| | | | | One aspect I always enjoy about machine learning is that questions often go back to the basics. The field essentially goes into an existential crisis every dozen years-rethinking our tools and asking foundational questions such as "why neural networks" or "why generative models".1 This was a theme in my conversations during NIPS 2016 last week, where a frequent topic was on the advantages of a Bayesian perspective to machine learning. Not surprisingly, this appeared as a big discussion point during the p... | |
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rpsychologist.com
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andrewjaffe.net
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| | | | | [AI summary] A technical discussion of Bayesian statistics, specifically debating the limitations of Bayesian model comparison and advocating for generalized frequentist approaches to model checking and falsification. | |
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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. | ||