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danieltakeshi.github.io | ||
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jaberkow.wordpress.com
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| | | | | Lately I have been making use of a continuous relaxation of discrete random variables proposed in two recent papers: The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables and Categorical Reparameterization with Gumbel-Softmax. I decided to write a blog post with some motivation of the method, as well as providing some minor clarification on... | |
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gregorygundersen.com
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| | | | | [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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www.randomservices.org
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| | | | | [AI summary] The text presents a comprehensive overview of the beta-Bernoulli process and its related statistical properties. Key concepts include: 1) The Bayesian estimator of the probability parameter $ p $ based on Bernoulli trials, which is $ rac{a + Y_n}{a + b + n} $, where $ a $ and $ b $ are parameters of the beta distribution. 2) The stochastic process $ s{Z} = rac{a + Y_n}{a + b + n} $, which is a martingale and central to the theory of the beta-Bernoulli process. 3) The distribution of the trial number of the $ k $th success, $ V_k $, which follows a beta-negative binomial distribution. 4) The mean and variance of $ V_k $, derived using conditional expectations. 5) The connection between the beta distribution and the negative binomial distributi... | |
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www.nicholas-ollberding.com
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| | | This is post is to introduce members of the Cincinnati Children's Hospital Medical Center R Users Group (CCHMC-RUG) to some of the functionality provided by Frank Harrell's Hmisc and rms packages for data description and predictive modeling. | ||
