# Bayesian Learning of Clique Tree Structure

arXiv: https://arxiv.org/abs/1708.07025 · HTML: https://mlsanity.com/paper/1708.07025/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2017.

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

- [Random Subspace Mixture Models for Interpretable Anomaly Detection](https://mlsanity.com/paper/2108.06283/) (2021)

## Abstract (arXiv)

The problem of categorical data analysis in high dimensions is considered. A discussion of the fundamental difficulties of probability modeling is provided, and a solution to the derivation of high dimensional probability distributions based on Bayesian learning of clique tree decomposition is presented. The main contributions of this paper are an automated determination of the optimal clique tree structure for probability modeling, the resulting derived probability distribution, and a corresponding unified approach to clustering and anomaly detection based on the probability distribution.