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 sander.ai
Addendum: quantifying our flawed intuitions - Sander Dieleman
| | fa.bianp.net
On the Convergence of the Unadjusted Langevin Algorithm
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| | The Langevin algorithm is a simple and powerful method to sample from a probability distribution. It's a key ingredient of some machine learning methods such as diffusion models and differentially private learning. In this post, I'll derive a simple convergence analysis of this method in the special case when the ...
| | gregorygundersen.com
Expectation of the Truncated Lognormal Distribution
1.7 parsecs away

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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.
| | www.randomservices.org
Continuous Distributions
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| | [AI summary] The text covers various topics in probability and statistics, including continuous distributions, empirical density functions, and data analysis. It discusses the uniform distribution, rejection sampling, and the construction of continuous distributions without probability density functions. The text also includes data analysis exercises involving empirical density functions for body weight, body length, and gender-specific body weight.
| | qchu.wordpress.com
Meditation on the Sylow theorems II | Annoying Precision
19.2 parsecs away

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| In Part I we discussed some conceptual proofs of the Sylow theorems. Two of those proofs involve reducing the existence of Sylow subgroups to the existence of Sylow subgroups of $latex S_n$ and $latex GL_n(\mathbb{F}_p)$ respectively. The goal of this post is to understand the Sylow $latex p$-subgroups of $latex GL_n(\mathbb{F}_p)$ in more detail and...