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Pregled bibliografske jedinice broj: 36999

Comparison of Methods for Reduction of Computational Complexity in Bayesian Networks


Bogunović, Nikola; Šmuc, Tomislav
Comparison of Methods for Reduction of Computational Complexity in Bayesian Networks // Computers in Intelligent Systems
Rijeka: MIPRO HU, 1999. str. 17-20 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Comparison of Methods for Reduction of Computational Complexity in Bayesian Networks

Autori
Bogunović, Nikola ; Šmuc, Tomislav

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Computers in Intelligent Systems / - Rijeka : MIPRO HU, 1999, 17-20

Skup
MIPRO99, XXII International Convention

Mjesto i datum
Opatija, Hrvatska, 17-21.05.1999

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Artificial intelligence; probabilistic reasoning; Bayesian networks

Sažetak
Bayesian networks offer great potential for use in automating large scale reasoning tasks, e.g. diagnostics. Unfortunately reasoning in richly interconnected Bayesian network is NP-hard. Hence, for many practical problems, exact computations are prohibitive. Therefore, approximate solutions are often the best that can be hoped for. Approximate algorithms are characterized by the nature of the bounds on the estimates they produce and by the reliability with which the exact answer lies within this bounds. It was shown that the evaluation of a Beyesian network within probably approximately correct bounds is also NP-hard. This paper explores some new and appealing approximation schemes for Bayesian networks in order to reduce the computational complexity of the inference process. The methods are analyzed from the theoretical viewpoint, and tested over a set of some well-known exemplar problems.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Projekt / tema
00980501

Ustanove
Institut "Ruđer Bošković", Zagreb