# Loss-Based Variational Bayes Prediction

arXiv: https://arxiv.org/abs/2104.14054 · HTML: https://mlsanity.com/paper/2104.14054/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2021.

## Built on by (most cited first, 4 of 4)

- [User-friendly introduction to PAC-Bayes bounds](https://mlsanity.com/paper/2110.11216/) (2021)
- [Generative Regression with IQ-BART](https://mlsanity.com/paper/2507.04168/) (2025)
- [Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model](https://mlsanity.com/paper/2408.13588/) (2024)
- [Variational predictive resampling](https://mlsanity.com/paper/2605.11168/) (2026)

## Abstract (arXiv)

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.