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erikbern.com | ||
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jaketae.github.io
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| | | | | "I think that's very unlikely." "No, you're probably right." | |
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blog.demofox.org
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| | | | | There is ~400 lines of standalone C++ code that implements the main ideas in this post. You can find it at: https://github.com/Atrix256/MetropolisMCMC In previous posts I showed how to generate random numbers from a specific distributing by using two techniques: Rejection Sampling: https://blog.demofox.org/2017/08/08/generating-random-numbers-from-a-specific-distribution-with-rejection-sampling/ Inverting the CDF: https://blog.demofox.org/2017/08/05/generating-random-numbers-from-a-specific-distribution-by-inverting-the-cdf/ This post will show how to do it... | |
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freerangestats.info
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| | | | | The success rate (proportion of times the true value is covered by the interval) of 95% confidence intervals from the bootstrap when estimating population standard deviation can be very poor for complex mixed distributions, such as real world weekly income from a modest sample size (<20,000). | |
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www.unite.ai
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| | | Some machine learning models belong to either the generative or discriminative model categories. Yet what is the difference between these two categories of models? What does it mean for a model to be discriminative or generative? The short answer is that generative models are those that include the distribution of the data set, returning a [] | ||
