/explore
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You are here 
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ddarmon.github.io | ||
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www.huber.embl.de
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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. | |
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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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brenocon.com
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| | | | | [AI summary] The provided text is a collection of comments and discussions from a blog post that originally criticized artificial neural networks (ANNs) in 2008. The comments reflect a range of opinions and debates about the relationship between machine learning (ML) and statistics, with some users defending ML techniques like support vector machines (SVMs), probabilistic graphical models, and deep learning, while others argue for the importance of statistical methods. There are also discussions about the need for better communication between disciplines, the limitations of ML approaches, and the importance of understanding the underlying assumptions of models. The text includes recommendations for textbooks and courses, such as 'All of Statistics' and Andre... | |
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lucatrevisan.wordpress.com
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| | | Today we will see how to use the analysis of the multiplicative weights algorithm in order to construct pseudorandom sets. The method will yield constructions that are optimal in terms of the size of the pseudorandom set, but not very efficient, although there is at least one case (getting an ``almost pairwise independent'' pseudorandom generator)... | ||
