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deut-erium.github.io | ||
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nelari.us
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| | | | | 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? | |
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blog.za3k.com
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| | | | | [AI summary] The article explains how to create a hardware random number generator by combining multiple entropy sources to mitigate backdoor risks, using examples like /dev/urandom, USB devices, and Raspberry Pi hardware. | |
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www.foonathan.net
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| | | | | The size of std::array is known at compile-time given the type. Yet it only provides a regular .size() member function: template struct array { constexpr std::size_t size() const { return N; } }; This is annoying if you're writing generic code that expects some sort of compile-time sized range. | |
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doomlab.github.io
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| | | Shiny Apps Each Shiny application is listed below, along with any relevant publication information. MOTE: Magnitude of the Effect Shiny App GitHub Research Paper This app allows you to calculate many effect sizes and their confidence intervals. We have included mean differences and variance overlap effect sizes, their formulas, easy to copy output in APA style, help videos on how to use our app, and code for R users. Alternatives to Null Hypothesis Significance Testing Shiny App GitHub Research Paper This app provides color visualization of the data from our paper that focuses on alternatives to NHST procedures. | ||
