Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

Regular Bayesian and frequentist approximations in statistics are studied within a unified framework. In particular it is shown how some higher-order likelihood-based approximations arise from their ...

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 ...

If the reported data of an experiment have been subject to selection, then inference from such data should be modified accordingly. We investigate the modification required to the face-value ...

The Engine for Likelihood-Free Inference is open to everyone, and it can help significantly reduce the number of simulator runs. The Engine for Likelihood-Free Inference is open to everyone, and it ...

Background Bayesian networks (BN) are directed acyclic graphs derived from empirical data that describe the dependency and probability structure. It may facilitate understanding of complex ...