Fahiem Bacchus, Adam Grove, Joseph Halpern, and Daphne Koller. Generating new beliefs from old. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pages 37--45, 1994. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Probabilistic Default Reasoning with Conditional Constraints - Lukasiewicz (2000) (1 citation) (Correct)
....reasoning. Other approaches to reasoning from statistical and subjective knowledge that allow for purely probabilistic subjective input knowledge use especially minimum cross entropy to combine statistical and subjective probability distributions (see in particular the work by Bacchus et al. [4] and Jaeger [44] Other less closely related work concerns the idea of combining ranking functions with probabilities. Spohn [75] briefly sketches the concept of probabilified OCFs, which associate with each proposition an ordered pair consisting of an ordinal and a real in . Blume et ....
Bacchus, F., A. Grove, J. Y. Halpern, and D. Koller: 1994a, `Generating new beliefs from old'. In: Proceedings UAI-94. pp. 37--45.
Minimum Cross-Entropy Reasoning: A Statistical Justification - Jaeger (1995) (Correct)
....is the one most frequently encountered. Entropy maximization is the most common rule of this type. A second type of probabilistic inference is given by the random worlds formalism of Bacchus et al. 1992 ] Here constraints on statistical probabilities are used to derive degrees of belief. In [ Bacchus et al. 1994 ] it is also shown how this method can be extended to make the resulting subjective probabilities also depend on given prior degrees of belief. It is this third kind of probabilistic inference, where information about both statistical and subjective probabilities are used to complete the set of ....
F. Bacchus, A. Grove, J.Y. Halpern, and D. Koller. Generating new beliefs from old. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, 1994.
Probability Update: Conditioning vs. Cross-Entropy - Grove, Halpern (1997) Self-citation (Grove Halpern) (Correct)
....constraint such as Pr(T ) 2=3 or the expected value of some random variable on S is 2=3 . We cannot condition on this information, since it is not an event in S. Yet it is clearly useful information. So how should we incorporate it There is in fact a rich literature on the subject (e.g. see [Bacchus, Grove, Halpern, and Koller 1994; Diaconis and Zabell 1982; Jeffrey 1983; Jaynes 1983; Paris and Vencovska 1992; Uffink 1995] Most proposals attempt to find the probability distribution that satisfies the new information and is in some sense the closest to the original distribution Pr. Certainly the best known and most ....
Bacchus, F., A. J. Grove, J. Y. Halpern, and D. Koller (1994). Generating new beliefs from old. In Proc. Tenth Annual Conference on Uncertainty Artificial Intelligence, pp.
From Statistical Knowledge Bases to Degrees of Belief - Bacchus, Grove, al. (1996) (46 citations) Self-citation (Bacchus Grove Halpern Koller) (Correct)
....of belief involve looking at sets of possible worlds. Thus, in order to handle such a statement appropriately, we would need to ensure that our probability distribution over the possible worlds satisfies the associated constraint. A number of different approaches to doing this are discussed in [BGHK94b] and shown to be essentially equivalent. Yet another approach for dealing with the learning problem is to use a variant of random worlds presented in [BGHK92] called the random propensities approach. Random worlds has a strong bias towards believing that exactly half the domain has any given ....
F. Bacchus, A. J. Grove, J. Y. Halpern, and D. Koller. Generating new beliefs from old. To appear in Proc. 10th Conference on Uncertainty in Artifical Intelligence (UAI '94), 1994.
Answering Queries from Statistics and Probabilistic Views - Dalvi, Suciu (2005) (Correct)
No context found.
Fahiem Bacchus, Adam Grove, Joseph Halpern, and Daphne Koller. Generating new beliefs from old. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pages 37--45, 1994.
Maximum Entropy Probabilistic Logic - Mark Paskin Computer (Correct)
No context found.
Bacchus, F.; Grove, A. J.; Halpern, J. Y.; and Koller, D. 1994. Generating new beliefs from old. In de Mantaras, R. L., and Poole, D., eds., Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 37--45. San Francisco, CA: Morgan Kaufmann.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC