# Batch Updating of a Posterior Tree Distribution over a Meta-Tree

arXiv: https://arxiv.org/abs/2303.09705 · HTML: https://mlsanity.com/paper/2303.09705/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Probability Distribution on Full Rooted Trees](https://mlsanity.com/paper/2109.12825/) (2021)

## Built on by (most cited first, 3 of 3)

- [Soft Bayesian Context Tree Models for Real-Valued Time Series](https://mlsanity.com/paper/2601.11079/) (2026)
- [Prediction Algorithms Achieving Bayesian Decision Theoretical Optimality Based on Decision Trees as Data Observation Processes](https://mlsanity.com/paper/2306.07060/) (2023)
- [Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation](https://mlsanity.com/paper/2601.16112/) (2026)

## Abstract (arXiv)

Previously, we proposed a probabilistic data generation model represented by an unobservable tree and a sequential updating method to calculate a posterior distribution over a set of trees. The set is called a meta-tree. In this paper, we propose a more efficient batch updating method.