On the behaviour of Bayesian credible intervals in partially identified models

On the Behaviour of Bayesian Credible Intervals in Partially Identified Models

MCMC Generated Bayesian Credible Intervals in Hierarchical Models

Under a Bayesian approach to a hierarchical model interest often lies in summarizing the posterior distribution of a parameter in order to enable easy comparison across prior structures This can be done for example through quantile or interval estimation When using an MCMC algorithm such as the Gibbs sampler to generate a sample from the posterior of interest calcu lations are often easier when...

Bayesian inference for partially identified models.

Identification can be a major issue in causal modeling contexts, and in contexts where observational studies have various limitations. Partially identified models can arise, whereby the identification region for a target parameter--the set of values consistent with the law of the observable data--is strictly contained in the set of a priori plausible values, but strictly contains the single tru...

on some bayesian statistical models in actuarial science with emphasis on claim count

چکیده ندارد.

Semi-parametric Bayesian Partially Identified Models based on Support Function*

We provide a comprehensive semi-parametric study of Bayesian partially identified econometric models. While the existing literature on Bayesian partial identification has mostly focused on the structural parameter, our primary focus is on Bayesian credible sets (BCS’s) of the unknown identified set and the posterior distribution of its support function. We construct a (two-sided) BCS based on t...