Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling
Full Text:
doubling-uai2009.pdf
A Bayesian belief network models a joint
distribution with an directed acyclic graph
representing dependencies among variables
and network parameters characterizing conditional
distributions. The parameters are
viewed as random variables to quantify uncertainty
about their values. Belief nets are
used to compute responses to queries; i.e.,
conditional probabilities of interest. A query
is a function of the parameters, hence a random
variable. Van Allen et al. (2001, 2008)
showed how to quantify uncertainty about a
query via a delta method approximation of
its variance. We develop more accurate approximations
for both query mean and variance.
The key idea is to extend the query
mean approximation to a "doubled network"
involving two independent replicates. Our
method assumes complete data and can be
applied to discrete, continuous, and hybrid
networks (provided discrete variables have
only discrete parents). We analyze several
improvements, and provide empirical studies
to demonstrate their effectiveness.
Citation
P. Hooper,
Y. Abbasi-Yadkori,
R. Greiner,
B. Hoehn.
"Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling".
Conference on Uncertainty in Artificial Intelligence (UAI), June 2009.
Keywords: |
machine learning, belief network, inference, variance |
Category: |
In Conference |
BibTeX
@incollection{Hooper+al:UAI09,
author = {Peter Hooper and Yasin Abbasi-Yadkori and Russ Greiner and Bret
Hoehn},
title = {Improved Mean and Variance Approximations for Belief Net Responses
via Network Doubling},
booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
year = 2009,
}
Last Updated: June 16, 2009
Submitted by Russ Greiner