Corpus ID: 88521219

A general measure of the impact of priors in Bayesian statistics via Stein's Method

@article{Ghaderinezhad2018AGM,
  title={A general measure of the impact of priors in Bayesian statistics via Stein's Method},
  author={Fatemeh Ghaderinezhad and Christophe Ley},
  journal={arXiv: Statistics Theory},
  year={2018}
}
We propose a measure of the impact of any two choices of prior distributions by quantifying the Wasserstein distance between the respective resulting posterior distributions at any fixed sample size. We illustrate this measure on the normal, Binomial and Poisson models. 
1 Citations
PR ] 2 7 M ay 2 02 1 Stein ’ s Method for Probability Distributions on S 1
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References

SHOWING 1-8 OF 8 REFERENCES
Distances between nested densities and a measure of the impact of the prior in Bayesian statistics
In this paper we propose tight upper and lower bounds for the Wasserstein distance between any two {{univariate continuous distributions}} with probability densities $p_1$ and $p_2$ having nestedExpand
Stein's method for comparison of univariate distributions
We propose a new general version of Stein's method for univariate distributions. In particular we propose a canonical definition of the Stein operator of a probability distribution {which is based onExpand
On the consistency of Bayes estimates
On etudie les proprietes de frequence des regles de Bayes avec une attention particuliere a la consistence
Bayesian Data Analysis
TLDR
Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided. Expand
On inconsistent Bayes estimates of location
On montre que dans certaines situations relativement naturelles les estimateurs de position bayesiens sont inconsistants
Use of exchangeable pairs in the analysis of simulations
The method of exchangeable pairs has emerged as an important tool in proving limit theorems for Poisson, normal and other classical approx- imations. Here the method is used in a simulation context.Expand
Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory
TLDR
This paper develops a Bayesian total error analysis methodology for hydrological models that allows the modeler to directly and transparently incorporate, test, and refine existing understanding of all sources of data uncertainty in a specific application, including both rainfall and runoff uncertainties. Expand
Bayesian analysis of input uncertainty in hydrological modeling: 2. Application
TLDR
This study considers a BATEA assessment of two North American catchments and assesses the performance of the conceptual Variable Infiltration Capacity model with and without accounting for input (precipitation) uncertainty. Expand