Tootfinder

Leyendo el artículo de Vicisitud y sordidez sobre el Metal no puedo mšs que estar de acuerdo con esta afirmación: "Cuando pruebas una Imperial Stout de 10 grados y te gusta, no vuelves a pedirte una Radler en la puta vida."
Larga vida al Imperial Stout.... y al metal
https://www.<…

Can language models boost the power of randomized experiments without statistical bias?
Xinrui Ruan, Xinwei Ma, Yingfei Wang, Waverly Wei, Jingshen Wang
https://arxiv.org/abs/2510.05545

Can language models boost the power of randomized experiments without statistical bias?
Randomized experiments or randomized controlled trials (RCTs) are gold standards for causal inference, yet cost and sample-size constraints limit power. Meanwhile, modern RCTs routinely collect rich, unstructured data that are highly prognostic of outcomes but rarely used in causal analyses. We introduce CALM (Causal Analysis leveraging Language Models), a statistical framework that integrates large language models (LLMs) predictions with established causal estimators to increase precision whil…

Generalized Taylor's Law for Dependent and Heterogeneous Heavy-Tailed Data
Pok Him Cheng, Joel E. Cohen, Hok Kan Ling, Sheung Chi Phillip Yam
https://arxiv.org/abs/2510.09562

Generalized Taylor's Law for Dependent and Heterogeneous Heavy-Tailed Data
Taylor's law, also known as fluctuation scaling in physics and the power-law variance function in statistics, is an empirical pattern widely observed across fields including ecology, physics, finance, and epidemiology. It states that the variance of a sample scales as a power function of the mean of the sample. We study generalizations of Taylor's law in the context of heavy-tailed distributions with infinite mean and variance. We establish the probabilistic limit and analyze the associated con…