On a deficiency of the FCI algorithm learning Bayesian from data

• Autorzy: Kłopotek M.
• Języki publikacji: EN

Abstrakty

EN

Causally insufficient structures (models with latent or hidden variables, or with confounding etc.) of joint probability distributions have been subject of intense study not oniy in statistics, but also in various AI systems. In AI, belief networks, being representations of joint probability distribution with an underlying directed acyclic graph structure, are paid special attention due to the fact that efficient reasoning (uncertainty propagation) methods have been developed for belief network structures. Algorithms have been therefore developed to acquire the belief network structure from data. As artifacts due to variable hiding negatively influence the performance of derived belief networks, models with latent variables have been studied and several algorithms for learning belief network structure under causal insufficiency have also been developed. Regrettably, some of them are known aiready to be erroneous (e.g. IC algorithm of [12]). This paper is devoted to another algorithm, the Fast Causal Inference (FCI) Algorithm of [17]. It is proven by a specially constructed example that this algorithm, as it stands in [17], is also erroneous. Fundamental reason for failure of this algorithm is the temporary introduction of non-real links between nodes of the network with the intention of later removal. While for trivial dependency structures these non-real links may be actually removed, this may not be the case for complex ones, e.g. for the case described in this paper. A remedy of this failure is proposed

Słowa kluczowe

EN

belief networks; discovery under causal insufficiency

PL

sieci bayesowskie

• Wydawca: De Gruyter
• Czasopismo: Demonstratio Mathematica
• Rocznik: 2000
• Tom: Vol. 33, nr 1
• Strony: 181--194
• Opis fizyczny: Bibliogr. 18 poz.

Twórcy

autor

Kłopotek M.

• Faculty of Mathematics and Information Sciences Warsaw University of Technology Pl. Politechniki 1 00-661 Warszawa,