We deal with strong consistency for Bayesian density estimation. An awkward consequence of inconsistency is described. It is pointed out that consistency at some density f_0 depends on the prior mass assigned to the ‘pathological’ set of those densities that are close to f_0, in a weak sense, and far apart from f_0, in a Hellinger sense. An analysis of these sets leads to the identification of the notion of ‘data tracking’. Specific examples in which this phenomenon cannot occur are discussed. When it can happen, we show how and where things can go wrong, thus providing more intuition about the sources of inconsistency.
Data tracking and the understanding of Bayesian consistency
LIJOI, ANTONIO;
2005-01-01
Abstract
We deal with strong consistency for Bayesian density estimation. An awkward consequence of inconsistency is described. It is pointed out that consistency at some density f_0 depends on the prior mass assigned to the ‘pathological’ set of those densities that are close to f_0, in a weak sense, and far apart from f_0, in a Hellinger sense. An analysis of these sets leads to the identification of the notion of ‘data tracking’. Specific examples in which this phenomenon cannot occur are discussed. When it can happen, we show how and where things can go wrong, thus providing more intuition about the sources of inconsistency.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
