# Logarithmic-Time Updates and Queries in Probabilistic Networks

arXiv: https://arxiv.org/abs/1408.1479 · HTML: https://mlsanity.com/paper/1408.1479/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2014.

## Built on by (most cited first, 2 of 2)

- [Nearest Neighbour with Bandit Feedback](https://mlsanity.com/paper/2306.13773/) (2023)
- [A Hierarchical Nearest Neighbour Approach to Contextual Bandits](https://mlsanity.com/paper/2312.09332/) (2023)

## Abstract (arXiv)

In this paper we propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks (causal trees and polytrees). In the conventional algorithms, new evidence in absorbed in time O(1) and queries are processed in time O(N), where N is the size of the network. We propose a practical algorithm which, after a preprocessing phase, allows us to answer queries in time O(log N) at the expense of O(logn N) time per evidence absorption. The usefulness of sub-linear processing time manifests itself in applications requiring (near) real-time response over large probabilistic databases.