Each of these methods contributes useful priors for "regular" one-parameter problems, and each prior can handle some challenging statistical models (with "irregularity" or several parameters). Each of these methods has been useful in Bayesian practice. Indeed, methods for constructing "objective" (alternatively, "default" or "ignorance") priors have been developed by avowed subjective (or "personal") Bayesians like James Berger (Duke University) and José-Miguel Bernardo (Universitat de València), simply because such priors are needed for Bayesian practice, particularly in science. The quest for "the universal method for constructing priors" continues to attract statistical theorists.
each of these methods contributes useful priors for "regular" one-parameter problems and each prior can handle some challenging statistical models (with "irregularity" or several parameters) each of these methods has been useful in bayesian practice indeed methods for constructing "objective" (alternatively "default" or "ignorance") priors have been developed by avowed subjective (or "personal") bayesians like james berger (duke university) and josé-miguel bernardo (universitat de valència) simply because such priors are needed for bayesian practice particularly in science the quest for "the universal method for constructing priors" continues to attract statistical theorists