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 …

The past two decades have seen a major expansion in the availability, size, and precision of
time-domain data sets in astronomy. Owing to their unique combination of flexibility …

We report multiple lines of evidence for a stochastic signal that is correlated among 67
pulsars from the 15 yr pulsar timing data set collected by the North American Nanohertz …

The paired measurement of RNA and surface proteins in single cells with cellular indexing
of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to …

Background Assessing the burden of COVID-19 on the basis of medically attended case
numbers is suboptimal given its reliance on testing strategy, changing case definitions, and …

Micronutrient deficiencies account for an estimated one million premature deaths annually,
and for some nations can reduce gross domestic product, by up to 11%, highlighting the …

Big data analytics (BDA) have been emerged as an important instrument to support firms
suffering from sustainability issues. BDA could enhance environmental performance of the …

The T2K experiment presents new measurements of neutrino oscillation parameters using
19.7 (16.3)× 10 20 protons on target (POT) in (anti-) neutrino mode at the far detector (FD) …

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

Computational modeling of behavior has revolutionized psychology and neuroscience. By
fitting models to experimental data we can probe the algorithms underlying behavior, find …