Jensen, F. V., Cautious propagation in bayesian networks, in Proceedings of the 11th Conference on Uncertainty in Arti#cial Intelligence, Montreal, Canada, 323#328, 1995. Home/Search Document Details and Download
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Simplifying Explanations in Bayesian Belief Networks - de Campos, Gámez, Moral (Correct)
....exponentially with the number of variables in the explanation set, the process could be intractable. In sensitivity analysis [7] the same problem exists, and the solution adopted by Jensen et al. was to use a modified scheme of inference in junction trees called cautious propagation (Jensen, [5]) Cautious propagation is a modification of HUGIN propagation into a Shafer Shenoy like architecture [15] it is less efficient than HUGIN, but combined with cautious entering of evidence it provides access to P (e 0 jh) for a great number of subsets e 0 . As P (x O jx 0 E ) P (x 0 E ....
Finn V. Jensen. Cautious propagation in Bayesian networks. In Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence (UAI--95), pages 323--328, Montreal, Quebec, Canada, 1995.
A Roadmap to Research on Bayesian Networks and other.. - Chrisman (1998) (2 citations) (Correct)
....How valuable would one additional piece of evidence be: How66] DBL90] Mat90] ZQP93a] HHM91] HHM93] JL94] Eza94] 2.6. 2 Fast Retraction of Evidence How does result change if one (or more) item(s) of evidence is not included (related also to Sensitivity Analysis) Daw92] Jen95] 2.6.3 Sensitivity Analysis How sensitive are answers to model probabilities: Kor90] HS93] Las93] CNKE93] NK91] NA91] Pro91] CS95] Derivatives: Bun95a] 2.6.4 K most probable cases Compute the K most probable configurations, rather than just the single most probable one: ....
F. V. Jensen. Cautious propagation in Bayesian networks. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, 1995.
Lazy Propagation in Junction Trees - Jensen (1998) (9 citations) Self-citation (Jensen) (Correct)
....the direction of the links in the network. By standard we mean the Lauritzen Spiegelhalter (Lauritzen Spiegelhalter 1988) the Shafer Shenoy (Shafer Shenoy 1990) and the Hugin (Jensen, Lauritzen Olesen 1990) algorithms and the various variations over these algorithms ( Shachter 1990) and (Jensen 1995)) These algorithms build a secondary structure (a junction tree or a join tree) by triangulating the (moralized) network. This structure can be used for propagation for all information scenaria. Therefore, the algorithms do not exploit independences induced by the evidence. That is, the ....
Jensen, F. (1995), Cautious propagation in Bayesian networks, in P. Besnard & S. Hanks, eds, `Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence', pp. 323--328.
Parallelization of Inference in Bayesian Networks - Madsen, Jensen (1999) (1 citation) Self-citation (Jensen) (Correct)
....inference in Bayesian networks. The more commonly known inference algorithms are the LauritzenSpiegelhalter (Lauritzen and Spiegelhalter, 1988) the Shafer Shenoy (Shafer and Shenoy, 1990) and the Hugin algorithms and variations over these methods such as for example (Shachter, 1990; Jensen, 1995). These methods were developed to efficiently calculate the posterior probability distributions for all variables in the Bayesian network. If the reasoning, on the other hand, is focused on a small subset of the variables other more efficient algorithms exist. The SPI algorithm (Li and D Ambrosio, ....
Jensen, F. V. (1995). Cautious Propagation in Bayesian Networks. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 323--328.
Lazy Propagation in Junction Trees - Madsen, Jensen (1998) (9 citations) Self-citation (Jensen) (Correct)
....use the direction of the links in the network. By standard we mean the LauritzenSpiegelhalter [Lauritzen and Spiegelhalter, 1988] the Shafer Shenoy [Shafer and Shenoy, 1990] and the Hugin [Jensen et al. 1990] algorithms and the various variations over these algorithms ( Shachter, 1990] and [Jensen, 1995]) These algorithms build a secondary structure (a junction tree or a join tree) by triangulating the (moralized) network. This structure can be used for propagation for all information scenaria. Therefore, the algorithms do not exploit independences induced by the evidence. That is, the ....
Jensen, F. V. (1995). Cautious propagation in Bayesian networks. In Besnard, P. and Hanks, S., editors, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 323--328.
Inference in Belief Networks: A Procedural Guide - Huang, Darwiche (1994) (30 citations) (Correct)
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Jensen, F. V., Cautious propagation in bayesian networks, in Proceedings of the 11th Conference on Uncertainty in Arti#cial Intelligence, Montreal, Canada, 323#328, 1995.
Predicting calving dates with Bayesian Networks - Dittmer (Correct)
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