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freerangestats.info
| | juliasilge.com
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| | A data science blog
| | www.tylermw.com
4.5 parsecs away

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| | www.cedricscherer.com
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| | A step-by-step tutorial explaining how my visualizations have evolved from a typical basic ggplot. Here, I transform a basic boxplot into a compelling and self-explanatory combination of a jittered dot strip plot and a lollipop plot.
| | jaketae.github.io
28.6 parsecs away

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| 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.