The 7th International Symposium on Intelligent Data Analysis

Parameter Learning for Bayesian Networks with Strict Qualitative Influences

author: Ad Feelders, Utrecht University

Description

We propose a new method for learning the parameters of a Bayesian network with qualitative influences. The proposed method aims to remove unwanted (context-specific) independencies that are created by the order-constrained maximum likelihood (OCML) estimator. This is achieved by averaging the OCML estimator with the fitted probabilities of a first-order logistic regression model. We show experimentally that the new learning algorithm does not perform worse than OCML, and resolves a large part of the independencies.

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*Slides*
0:00	Parameter Learning for Bayesian Networks with Strict Qualitative Influences
0:46	Motivation
2:19	Qualitative Influences pt 1
3:38	Qualitative Influences pt 2
5:49	OCML Estimates Create Independencies
8:50	Specifying Minimum Margins
9:51	Our Proposal
11:00	How This (Sometimes) Solves the Problem
12:18	What If an LR Coefficient Has the Wrong Sign?
13:17	How to Determine the Value?
13:56	Results on Artificial Data
15:26	Results on Real Data
16:23	How Many Independencies Could We Fix?
16:53	Conclusions

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