Bayesian graphical models provide a principled framework for representing complex dependency structures among multivariate variables by combining graph theory with probabilistic inference. In these ...

Inferring group norms is crucial for adapting behaviors in novel situations, but its underlying basis and computational account remain unclear. This study manipulated the prevalence of norm-consistent ...

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 ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...