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 introduced new dimension and model reduction methods that extend optimal Bayesian inference to problems with ...

Researchers have developed an importance-sampling-based black box variational inference algorithm that reuses earlier ...

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 ...

We will take a deep dive into reliable AI-related papers published on arXiv around the last day (around October 21, 2026), ...

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 ...

Probabilities that created rebels, hidden signatures, unrecorded drivers, inbox gatekeepers, and those who predict the ...

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 ...

Our world view and resultant actions are often driven by a simple theorem, devised in secret more than 150 years ago by a quiet English mathematician and theologian, Thomas Bayes, and only published ...