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www.panix.com | ||
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sriku.org
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| | | | | [AI summary] The article explains how to generate random numbers that follow a specific probability distribution using a uniform random number generator, focusing on methods involving inverse transform sampling and handling both continuous and discrete cases. | |
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poissonisfish.com
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| | | | | 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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aakinshin.net
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| | | | | In [[carling-outlier-detector]], I evaluated the probability of outlier detection for samples from the Normal distribution across different outlier detectors. I performed numerical simulations for small sample sizes, then confidently extrapolated the result... | |
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almostsuremath.com
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| | | The aim of this post is to motivate the idea of representing probability spaces as states on a commutative algebra. We will consider how this abstract construction relates directly to classical probabilities. In the standard axiomatization of probability theory, due to Kolmogorov, the central construct is a probability space $latex {(\Omega,\mathcal F,{\mathbb P})}&fg=000000$. This consists... | ||
