David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin, Bonsoo Koo
arXiv 29 Apr 2021 · Statistics — Methodology · publishedJournal of Computational and Graphical Statistics (2024) · 8 citations (OpenAlex)
arXiv:2104.14054 · PDF · DOI · OpenAlex · Extracted main text
We propose a new approach to Bayesian prediction that caters for models with a large number of parameters and is robust to model misspecification. Given a class of high-dimensional (but parametric) predictive models, this new approach constructs a posterior predictive using a variational approximation to a generalized posterior that is directly focused on predictive accuracy. The theoretical behavior of the new prediction approach is analyzed and a form of optimality demonstrated. Applications to both simulated and empirical data using high-dimensional Bayesian neural network and autoregressive mixture models demonstrate that the approach provides more accurate results than various alternatives, including misspecified likelihood-based predictions.
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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 | Ruben Loaiza-Maya, Gael M Martin, and David T Frazier (2020) Focused Bayesian prediction self | 1.000 | 6 | 3 | 100% |
| 2 | Tilmann Gneiting and Adrian E Raftery (2007) Strictly proper scoring rules, prediction, and estimation | 1.000 | 5 | 3 | 100% |
| 3 | Pier Giovanni Bissiri, Chris C Holmes, and Stephen G Walker (2016) A general framework for updating belief distributions | 0.928 | 4 | 3 | 100% |
| 4 | Nicholas Syring and Ryan Martin (2019) Calibrating general posterior credible regions | 0.843 | 3 | 3 | 100% |
| 5 | Federica Giummolè, Valentina Mameli, Erlis Ruli, and Laura Ventura (2017) Objective Bayesian inference with proper scoring rules | 0.843 | 3 | 3 | 100% |
| 6 | Jeffrey W Miller (2021) Asymptotic normality, concentration, and coverage of generalized posteriors | 0.737 | 3 | 3 | 67% |
| 7 | Jeremias Knoblauch, Jack Jewson, and Theodoros Damoulas (2019) Generalized variational inference: Three arguments for deriving new posteriors | 0.737 | 3 | 2 | 100% |
| 8 | Pei-Shien Wu and Ryan Martin (2021) Calibrating generalized predictive distributions | 0.737 | 3 | 2 | 100% |
| 9 | Slawek Smyl (2020) A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting | 0.644 | 4 | 1 | 100% |
| 10 | David Blackwell and Lester Dubins (1962) Merging of opinions with increasing information | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 54 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bayesian Forecasting in Economics and Finance: A Modern Review | 0.511 | 2 | 1 |
| 2 | Forecasting: theory and practice | 0.405 | 1 | 1 |
| 3 | Variational Bayes in State Space Models: Inferential and Predictive Accuracy | 0.405 | 1 | 1 |