David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin
arXiv 23 Jun 2021 · Statistics — Methodology · publishedJournal of Computational and Graphical Statistics (2022) · 14 citations (OpenAlex)
arXiv:2106.12262 · PDF · DOI · OpenAlex · Extracted main text
Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that, in terms of accuracy on fixed parameters, there is a clear hierarchy in terms of the methods, with approaches that do not approximate the states yielding superior accuracy over methods that do. We also document numerically that the inferential discrepancies between the various methods often yield only small discrepancies in predictive accuracy over small out-of-sample evaluation periods. Nevertheless, in certain settings, these predictive discrepancies can become meaningful over a longer out-of-sample period. This finding indicates that the invariance of predictive results to inferential inaccuracy, which has been an oft-touted point made by practitioners seeking to justify the use of variational inference, is not ubiquitous and must be assessed on a case-by-case basis.
appendix boundary found by appendix_command · 56% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Westling, T. and McCormick, T (2019) Beyond prediction: A framework for inference with variational approximations in mixture models | 1.000 | 5 | 3 | 100% |
| 2 | Koop, G. and Korobilis, D (2020) Bayesian dynamic variable selection in high dimensions | 0.928 | 5 | 3 | 80% |
| 3 | Chan, J. C. and Yu, X (2020) Fast and accurate variational inference for large Bayesian vars with stochastic volatility | 0.894 | 7 | 5 | 71% |
| 4 | Loaiza-Maya, R., Smith, M. S., Nott, D. J., and Danaher, P. J (2021) Fast and accurate variational inference for models with many latent variables self | 0.822 | 9 | 5 | 56% |
| 5 | Tran, M.-N., Nott, D. J., and Kohn, R (2017) Variational Bayes with intractable likelihood | 0.811 | 4 | 2 | 100% |
| 6 | Quiroz, M., Nott, D. J., and Kohn, R (2018) Gaussian variational approximation for high-dimensional state space models | 0.794 | 12 | 5 | 50% |
| 7 | Lancaster, T (2000) The incidental parameter problem since 1948 | 0.644 | 2 | 2 | 100% |
| 8 | Carter, C. K. and Kohn, R (1994) On Gibbs sampling for state space models | 0.511 | 3 | 2 | 33% |
| 9 | Primiceri, G. E (2005) Time varying structural vector autoregressions and monetary policy | 0.511 | 3 | 2 | 33% |
| 10 | Blei, D. M., Kucukelbir, A., and McAuliffe, J. D (2017) Variational inference: A review for statisticians | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.