In the Bayesian framework, one updates his or her prior beliefs using the data obtained in a given study.

It was thus realized early on that the Bayesian statistical framework holds the potential to lead to insights into the function of the nervous system.

Bootstrapping can be interpreted in a Bayesian framework using a scheme that creates new datasets through reweighting the initial data.

Philosophers and scientists who follow the Bayesian framework for inference use the mathematical rules of probability to find this best explanation.

Existing methodologies for inverse uncertainty quantification are mostly under the Bayesian framework.

In the Bayesian framework, these factors correspond to prior probabilities and conditional probabilities, respectively.

This is a formal inductive framework that combines algorithmic information theory with the Bayesian framework.

In a Bayesian probabilistic framework is considered the proportion of individuals in a target population belonging to the ith stratum.

Within the Bayesian framework there is no risk of error since hypotheses are not accepted or rejected; instead they are assigned probabilities.

A Bayesian framework is also used for neural networks.