Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
EPISTEMIC IRRELEVANCE IN CREDAL NETS: THE CASE OF IMPRECISE MARKOV TREES
 

Summary: EPISTEMIC IRRELEVANCE IN CREDAL NETS: THE CASE OF IMPRECISE
MARKOV TREES
GERT DE COOMAN, FILIP HERMANS, ALESSANDRO ANTONUCCI, AND MARCO ZAFFALON
ABSTRACT. We focus on credal nets, which are graphical models that generalise Bayesian
nets to imprecise probability. We replace the notion of strong independence commonly
used in credal nets with the weaker notion of epistemic irrelevance, which is arguably more
suited for a behavioural theory of probability. Focusing on directed trees, we show how to
combine the given local uncertainty models in the nodes of the graph into a global model,
and we use this to construct and justify an exact message-passing algorithm that computes
updated beliefs for a variable in the tree. The algorithm, which is linear in the number of
nodes, is formulated entirely in terms of coherent lower previsions, and is shown to satisfy a
number of rationality requirements. We supply examples of the algorithm's operation, and
report an application to on-line character recognition that illustrates the advantages of our
approach for prediction. We comment on the perspectives, opened by the availability, for
the first time, of a truly efficient algorithm based on epistemic irrelevance.
1. INTRODUCTION
The last twenty years have witnessed a rapid growth of graphical models in the fields
of artificial intelligence and statistics. These models combine graphs and probability to
address complex multivariate problems in a variety of domains, such as medicine, finance,
risk analysis, defence, and environment, to name just a few.

  

Source: Antonucci, Alessandro - Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)

 

Collections: Computer Technologies and Information Sciences