Work

Computing quantities of interest and their uncertainty using Bayesian simulation

Year: 2022

Type: article

Abstract: When analyzing data, researchers are often less interested in the parameters of statistical models than in functions of these parameters such as predicted values. Here we show that Bayesian simulation with Markov-Chain Monte Carlo tools makes it easy to compute these quantities of interest with their uncertainty. We illustrate how to produce customary and relatively new quantities of interest such as variable importance ranking, posterior predictive data, difficult marginal effects, and model comparison statistics to allow researchers to report more informative results.

Source: Political Science Research and Methods

DOI: 10.1017/psrm.2022.18

Authors: Andreas Murr, Richard Traunmüller, Jeff Gill

Institutions: University of Warwick, University of Mannheim, American University

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,255

Funder: University of Warwick