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ddarmon.github.io | ||
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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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statsandr.com
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| | | | | This article explains in details what is the normal or Gaussian distribution, its importance in statistics and how to test if your data is normally distributed | |
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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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almostsuremath.com
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| | | According to Kolmogorov's axioms, to define a probability space we start with a set and an event space consisting of a sigma-algebra F? on ?. A probability measure on this gives the probability space (?,?F?,?), on which we can define random variables as measurable maps from to the reals or other measurable... | ||
