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pro vyhledávání: '"Kjærulff, Uffe"'
Autor:
Kjærulff, Uffe
A computational scheme for reasoning about dynamic systems using (causal) probabilistic networks is presented. The scheme is based on the framework of Lauritzen and Spiegelhalter (1988), and may be viewed as a generalization of the inference methods
Externí odkaz:
http://arxiv.org/abs/1303.5407
Autor:
Kjærulff, Uffe
The paper presents a method for reducing the computational complexity of Bayesian networks through identification and removal of weak dependencies (removal of links from the (moralized) independence graph). The removal of a small number of links may
Externí odkaz:
http://arxiv.org/abs/1302.6825
Autor:
Kjærulff, Uffe
Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of a hybrid between Monte-Carlo sampling methods and exact local computations in junction trees. Utilizing the strengths of both methods, such hybrid inference methods has the p
Externí odkaz:
http://arxiv.org/abs/1302.4968
Autor:
Kjærulff, Uffe
The efficiency of inference in both the Hugin and, most notably, the Shafer-Shenoy architectures can be improved by exploiting the independence relations induced by the incoming messages of a clique. That is, the message to be sent from a clique can
Externí odkaz:
http://arxiv.org/abs/1302.1553
Autor:
Meek, Christopher, Kjaerulff, Uffe
This is the Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, which was held in Acapulco, Mexico, August 7-10 2003
Externí odkaz:
http://arxiv.org/abs/1301.4606
When using Bayesian networks for modelling the behavior of man-made machinery, it usually happens that a large part of the model is deterministic. For such Bayesian networks deterministic part of the model can be represented as a Boolean function, an
Externí odkaz:
http://arxiv.org/abs/1301.3880
Autor:
Kjærulff, Uffe, van der Gaag, Linda C.
To investigate the robustness of the output probabilities of a Bayesian network, a sensitivity analysis can be performed. A one-way sensitivity analysis establishes, for each of the probability parameters of a network, a function expressing a posteri
Externí odkaz:
http://arxiv.org/abs/1301.3868
Autor:
Castillo, Enrique ∗, Kjærulff, Uffe
Publikováno v:
In Reliability Engineering and System Safety 2003 79(2):139-148
Publikováno v:
Kjærulff, U B & Madsen, A L 2008, Bayesian Networks and Influence Diagrams : A Guide to Construction and Analysis . Information Science and Statistics, Springer Publishing Company, New York .
Kjærulff, U B & Madsen, A L 2013, Bayesian Networks and Influence Diagrams : A Guide to Construction and Analysis . Information Science and Statistics, bind 22, 2. udg, Springer VS, New York . < http://link.springer.com/book/10.1007/978-1-4614-5104-4 >
Kjærulff, U B & Madsen, A L 2013, Bayesian Networks and Influence Diagrams : A Guide to Construction and Analysis . Information Science and Statistics, bind 22, 2. udg, Springer VS, New York . < http://link.springer.com/book/10.1007/978-1-4614-5104-4 >
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, pred
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3a5c86c9f8199dbcb61f07aef8ec5947
https://vbn.aau.dk/da/publications/c37b4810-a242-11dc-8188-000ea68e967b
https://vbn.aau.dk/da/publications/c37b4810-a242-11dc-8188-000ea68e967b