ON DUAL EXPRESSION OF PRIOR INFORMATION IN BAYESIAN PARAMETER ESTIMATION (original) (raw)

In Bayesian parameter estimation, a priori information can be used to shape the prior density of unknown parameters of the model. When chosen in a conjugate, selfreproducing form, the prior density of parameters is nothing but a model-based transform of a certain "prior" density of observed data. This observation suggests two possible ways of expressing a priori knowledge-in terms of parameters of a particular model and in terms of data entering the model. The latter way turns out useful when dealing with statistical models whose parameters lack a direct physical interpretation. In practice, the amount of a priori information is usually not sufficient for complete specification of the prior density of data. The paper shows an information-based way of converting such incomplete information into the prior density of unknown parameters.