# Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains

arXiv: https://arxiv.org/abs/1302.4957 · HTML: https://mlsanity.com/paper/1302.4957/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2013.

## Built on by (most cited first, 21 of 21)

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- [The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions](https://mlsanity.com/paper/2205.02602/) (2022)
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- [Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks](https://mlsanity.com/paper/2111.01853/) (2021)
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- [Causal Markov Boundaries](https://mlsanity.com/paper/2103.07560/) (2021)
- [Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model](https://mlsanity.com/paper/2303.08572/) (2023)
- [Optimal Particle-based Approximation of Discrete Distributions (OPAD)](https://mlsanity.com/paper/2412.00545/) (2024)
- [Less Greedy Equivalence Search](https://mlsanity.com/paper/2506.22331/) (2025)
- [Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions](https://mlsanity.com/paper/2105.03248/) (2021)
- [Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known](https://mlsanity.com/paper/2105.05395/) (2021)
- [Likelihoods and Parameter Priors for Bayesian Networks](https://mlsanity.com/paper/2105.06241/) (2021)
- [Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering](https://mlsanity.com/paper/2308.06399/) (2023)
- [Exact discovery is polynomial for certain sparse causal Bayesian networks](https://mlsanity.com/paper/2406.15012/) (2024)

## Abstract (arXiv)

We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both domains. We then use this metric in combination with well-known statistical facts about the Dirichlet and normal--Wishart distributions to derive our metrics for discrete and Gaussian domains.