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 sander.ai
Addendum: quantifying our flawed intuitions - Sander Dieleman
| | 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
1.5 parsecs away

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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.
| | fa.bianp.net
On the Convergence of the Unadjusted Langevin Algorithm
1.1 parsecs away

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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 ...
| | alexanderetz.com
Understanding Bayes: A Look at the Likelihood | The Etz-Files
17.6 parsecs away

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| [This post has been updated and turned into a paper to be published in AMPPS] Much of the discussion in psychology surrounding Bayesian inference focuses on priors. Should we embrace priors, or should we be skeptical? When are Bayesian methods sensitive to specification of the prior, and when do the data effectively overwhelm it? Should...