The article explores a method called prior swapping, which allows for quickly transforming inference results from one prior to another. This method can be more efficient than traditional approaches like importance sampling, especially when dealing with different types of priors. By using prior swapping, researchers can apply simpler inference techniques to certain models and update prior information after making initial inferences. The method has been tested on various models and priors, showing promising results.