This Primer examines Skilling’s nested sampling algorithm for Bayesian inference and, more broadly, multidimensional integration. The principles of nested sampling are summarize ...
Researchers have developed an importance-sampling-based black box variational inference algorithm that reuses earlier ...
Researchers have introduced new dimension and model reduction methods that extend optimal Bayesian inference to problems with ...
In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing ...
This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely ...
Understanding Bayesian inference through a drinking invitationHow does probability change with new information?Today's goal:Understand Bayesian inference using familiar problems and connect it to ...
How does one model a simple cell-signaling pathway? Consider a simple example consisting of a stimulant, an extracellular signal, an inhibitor of the signal, a G protein–coupled receptor, a G protein ...
In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...
We will take a deep dive into reliable AI-related papers published on arXiv around the last day (around October 21, 2026), ...
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