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gregorygundersen.com | ||
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austinrochford.com
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| | | | | While preparing the first in an upcoming series of posts on multi-armed bandits, I realized that a post diving deep on a simple Monte Carlo estimate of $\pi$ would be a useful companion, so here it is | |
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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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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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peterbloem.nl
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| | | [AI summary] The text provides an in-depth explanation of the Fundamental Theorem of Algebra, which states that every non-constant polynomial of degree $ n $ has exactly $ n $ roots in the complex number system, counting multiplicities. It walks through the proof by first establishing that every polynomial has at least one complex root (using the properties of continuous functions and the complex plane), then using polynomial division to factor the polynomial into linear factors, and finally addressing the nature of roots (real vs. complex) and their multiplicities. The text also touches on the conjugate root theorem, which explains why complex roots of polynomials with real coefficients come in conjugate pairs. | ||
