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FIRST INTERNATIONAL
SYMPOSIUM ON

IMPRECISE PROBABILITIES AND THEIR APPLICATIONS

####
Ghent, Belgium

30 June - 2 July 1999

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** ELECTRONIC PROCEEDINGS **

## Stefan Arnborg

# Learning in prevision Space

** Abstract **

We investigate some problems related to implementation of uncertainty
management, in particular the handling of computational and conceptual
difficulties that easily appear in complex problems. The uncertainty
polytope resulting from a set of inequality judgments on probabilities
and means in a problem has very high dimension, but
can be
represented by a projection on a low-dimensional space if the judgments
are structured
into a graph with low tree-width. With this representation many
judgments of independence become vacuous.
The uncertainty polytope is high-dimensional and thus
difficult to grasp or visualize.
We propose a method to
sample uniformly and efficiently
from the polytope, as a means to obtain various summaries not
obtainable by linear programming, such as
volume, center of gravity, principal axes, etc.

** Keywords. ** Learning, uncertainty, decomposition, uncertainty polytope

The paper is available in the following formats:

** Authors addresses: **

KTH

Nada

SE-100 44 Stockholm

Sweden

** E-mail addresses: **

** Related Web Sites **

Stefan Arnborg

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