Computing quantities of interest and their uncertainty using Bayesian simulation
Bibliographic Data
| ID | 6342292 |
|---|---|
| Authors | Andreas Erwin Murr (0000-0002-9536-0118, University of Warwick, corresponding author), Richard Traunmüller (0000-0001-9487-091X, University of Mannheim), Jeff Gill (0000-0002-8592-8996, American University) |
| Year | 2023 |
| Volume | 11 |
| Issue | 3 |
| Pages | 623-632 |
| Publication date | 2023-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Science Research and Methods (JOURNAL) |
| Journal identifiers | ISSN: 2049-8470 • E-ISSN: 2049-8489 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/psrm.2022.18 |
| OpenAlex | W4224879026 |
| Language | EN |
| Citations received | 2 |
| References cited | 5 |
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
Bayesian inference · Bayesian probability · Econometrics · Machine learning · Markov chain · Markov chain Monte Carlo · Monte Carlo method · Ranking (information retrieval · Statistics · Variable (mathematics · Computer Science · Electoral Systems and Political Participation · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Artificial Intelligence
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |