PBWlike
PBWlike is a software tool designed for performing parallelized Bayesian model averaging. It allows users to explore multiple statistical models simultaneously and estimate the average predictive performance of these models. The core functionality of PBWlike revolves around averaging predictions from a set of candidate models, weighted by their estimated posterior probabilities. This approach aims to provide a more robust prediction than relying on a single, best-fitting model, as it accounts for model uncertainty.
The tool is particularly useful in situations where the true underlying data-generating process is unknown or