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www.countbayesie.com | ||
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www.randomservices.org
[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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seeing-theory.brown.edu
A probability distribution specifies the relative likelihoods of all possible outcomes. |
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jaketae.github.io
So far on this blog, we have looked the mathematics behind distributions, most notably binomial, Poisson, and Gamma, with a little bit of exponential. These distributions are interesting in and of themselves, but their true beauty shines through when we analyze them under the light of Bayesian inference. In today's post, we first develop an intuition for conditional probabilities to derive Bayes' theorem. From there, we motivate the method of Bayesian inference as a means of understanding probability. |
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www.fredrikmeyer.net
Recently me and some friends were discussing first countability, and noticed that Wikipedia gave an example of space that was not first countable, but with n... |
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