.png){kind=icon}
.png){kind=icon}
Return
On Bayesian consistency
.png){kind=icon}
OA
AI
DOI：10.1111/1467-9868.00314.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We consider a sequence of posterior distributions based on a data-dependent prior (which we shall refer to as a pseudoposterior distribution) and establish simple conditions under which the sequence is Hellinger consistent. It is shown how investigations into these pseudoposteriors assist with the understanding of some true posterior distributions, including Polya trees, the infinite dimensional exponential family and mixture models.
Keywords:
asymptotics
Bayesian sieve
Bayes nonparametrics
consistency
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3.6
Papers:
1.5K
Citations:
3.2W
Organization
No organization information available