DeepAI AI Chat
Log In Sign Up

Learning networks determined by the ratio of prior and data

by   Maomi Ueno, et al.

Recent reports have described that the equivalent sample size (ESS) in a Dirichlet prior plays an important role in learning Bayesian networks. This paper provides an asymptotic analysis of the marginal likelihood score for a Bayesian network. Results show that the ratio of the ESS and sample size determine the penalty of adding arcs in learning Bayesian networks. The number of arcs increases monotonically as the ESS increases; the number of arcs monotonically decreases as the ESS decreases. Furthermore, the marginal likelihood score provides a unified expression of various score metrics by changing prior knowledge.


Robust learning Bayesian networks for prior belief

Recent reports have described that learning Bayesian networks are highly...

On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter

BDeu marginal likelihood score is a popular model selection criterion fo...

An Empirical-Bayes Score for Discrete Bayesian Networks

Bayesian network structure learning is often performed in a Bayesian set...

Asymptotic Model Selection for Naive Bayesian Networks

We develop a closed form asymptotic formula to compute the marginal like...

Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models

We present and implement two algorithms for analytic asymptotic evaluati...

Dirichlet Bayesian Network Scores and the Maximum Entropy Principle

A classic approach for learning Bayesian networks from data is to select...

Determination of Physical and Mechanical properties of Sugarcane Single-Bud Billet

Determining the physical and mechanical properties of sugarcane single-b...