# Inference for growing trees

arXiv: https://arxiv.org/abs/1910.04788 · HTML: https://mlsanity.com/paper/1910.04788/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Estimating the history of a random recursive tree](https://mlsanity.com/paper/2403.09755/) (2024)

## Abstract (arXiv)

One can often make inferences about a growing network from its current state alone. For example, it is generally possible to determine how a network changed over time or pick among plausible mechanisms explaining its growth. In practice, however, the extent to which such problems can be solved is limited by existing techniques, which are often inexact, inefficient, or both. In this article we derive exact and efficient inference methods for growing trees and demonstrate them in a series of applications: network interpolation, history reconstruction, model fitting, and model selection.