# Loss Bounds and Time Complexity for Speed Priors

arXiv: https://arxiv.org/abs/1604.03343 · HTML: https://mlsanity.com/paper/1604.03343/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [Learning Universal Predictors](https://mlsanity.com/paper/2401.14953/) (2024)
- [AI Agents as Universal Task Solvers](https://mlsanity.com/paper/2510.12066/) (2025)
- [Intelligence and Unambitiousness Using Algorithmic Information Theory](https://mlsanity.com/paper/2105.06268/) (2021)
- [Parsimonious Inference](https://mlsanity.com/paper/2103.02165/) (2021)
- [Understanding Generalization Requires Universal Induction](https://mlsanity.com/paper/2609.34458/) (2026)

## Abstract (arXiv)

This paper establishes for the first time the predictive performance of speed priors and their computational complexity. A speed prior is essentially a probability distribution that puts low probability on strings that are not efficiently computable. We propose a variant to the original speed prior (Schmidhuber, 2002), and show that our prior can predict sequences drawn from probability measures that are estimable in polynomial time. Our speed prior is computable in doubly-exponential time, but not in polynomial time. On a polynomial time computable sequence our speed prior is computable in exponential time. We show better upper complexity bounds for Schmidhuber's speed prior under the same conditions, and that it predicts deterministic sequences that are computable in polynomial time; however, we also show that it is not computable in polynomial time, and the question of its predictive properties for stochastic sequences remains open.