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sriku.org
| | poissonisfish.com

Someof the most fundamental functions in R, in my opinion, are those that deal with probability distributions. Whenever you compute a P-value you relyon a probability distribution, and there are many types out there. In this exercise I will cover four: Bernoulli, Binomial, Poisson, and Normal distributions. Let me begin with some theory first: Bernoulli...
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| | gregorygundersen.com

[AI summary] The blog post derives the expected value of a left-truncated lognormal distribution, explaining the mathematical derivation and validating it with Monte Carlo simulations.
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| | nelari.us

In inverse transform sampling, the inverse cumulative distribution function is used to generate random numbers in a given distribution. But why does this work? And how can you use it to generate random numbers in a given distribution by drawing random numbers from any arbitrary distribution?
0.7 parsecs

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| | ddarmon.github.io


22.1 parsecs

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