Work

Error Estimation in Approximate Bayesian Belief Network Inference

Year: 2013

Type: preprint

Abstract: We can perform inference in Bayesian belief networks by enumerating instantiations with high probability thus approximating the marginals. In this paper, we present a method for determining the fraction of instantiations that has to be considered such that the absolute error in the marginals does not exceed a predefined value. The method is based on extreme value theory. Essentially, the proposed method uses the reversed generalized Pareto distribution to model probabilities of instantiations below a given threshold. Based on this distribution, an estimate of the maximal absolute error if instantiations with probability smaller than u are disregarded can be made.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1302.4934

Authors: Castillo, Enrique F., Bouckaert, Remco R., Sarabia, Jose M., Solares, Cristina

Institutions: Universidad de Cantabria, University of Applied Sciences Utrecht, Utrecht University

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Universidad de Cantabria

OpenAlex ID: W1507255403

Cites: 17

Cited by: 3

Related to: 20