Bayesian statistics and modelling
Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …
available knowledge about parameters in a statistical model is updated with the information …
An extensive experimental survey of regression methods
Regression is a very relevant problem in machine learning, with many different available
approaches. The current work presents a comparison of a large collection composed by 77 …
approaches. The current work presents a comparison of a large collection composed by 77 …
Treatment-related adverse events of PD-1 and PD-L1 inhibitors in clinical trials: a systematic review and meta-analysis
Importance Programmed cell death (PD-1) and programmed cell death ligand 1 (PD-L1)
inhibitors have been increasingly used in cancer therapy. Understanding the treatment …
inhibitors have been increasingly used in cancer therapy. Understanding the treatment …
The bouba/kiki effect is robust across cultures and writing systems
The bouba/kiki effect—the association of the nonce word bouba with a round shape and kiki
with a spiky shape—is a type of correspondence between speech sounds and visual …
with a spiky shape—is a type of correspondence between speech sounds and visual …
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences
Motivation In RNA-seq differential expression analysis, investigators aim to detect those
genes with changes in expression level across conditions, despite technical and biological …
genes with changes in expression level across conditions, despite technical and biological …
Deep evidential regression
Deterministic neural networks (NNs) are increasingly being deployed in safety critical
domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this …
domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this …
Embers of autoregression: Understanding large language models through the problem they are trained to solve
RT McCoy, S Yao, D Friedman, M Hardy… - ar** clinical prediction models. Developers of such models often rely on an Events …
Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses
NP Lemoine - Oikos, 2019 - Wiley Online Library
Throughout the last two decades, Bayesian statistical methods have proliferated throughout
ecology and evolution. Numerous previous references established both philosophical and …
ecology and evolution. Numerous previous references established both philosophical and …