The complexity of finding kth most probable explanations in probabilistic networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6543 LNCS, Issue , 2011, Pages 356-367

  Kwisthout, Johan H P ^a   Bodlaender, Hans L ^a   Van Der Gaag, Linda C ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

DIRECTED ACYCLIC GRAPHS; INTERMEDIATE NODE; MODEL UNCERTAINTIES; MOST PROBABLE EXPLANATION; POSITIVE INTEGERS; PROBABILISTIC NETWORK; SPECIFIC VALUES; UNOBSERVABLE;

DECISION SUPPORT SYSTEMS; UNCERTAINTY ANALYSIS;

COMPUTER SCIENCE;

EID: 78751654486     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-18381-2_30     Document Type: Conference Paper

References (15)

1. Charitos, T., van der Gaag, L.C., Visscher, S., Schurink, C.A.M., Lucas, P.J.F.: A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients. Expert Systems with Applications 26, 1249-1258 (2009)
2. Geenen, P.L., Elbers, A.R.W., van der Gaag, L.C., Loeffen, W.L.A.: Development of a probabilistic network for clinical detection of classical swine fever. In: Proceedings of the 11th Symposium of the International Society for Veterinary Epidemiology and Economics, Cairns, Australia, pp. 667-669 (2006)
3. Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, Palo Alto (1988)
4. Roth, D.: On the hardness of approximate reasoning. Artificial Intelligence 82, 273-302 (1996)
5. Shimony, S.E.: Finding MAPs for belief networks is NP-hard. Artificial Intelligence 68, 399-410 (1994)
6. Bodlaender, H.L., van den Eijkhof, F., van der Gaag, L.C.: On the complexity of the MPA problem in probabilistic networks. In: van Harmelen, F. (ed.) Proceedings of the 15th European Conference on Artificial Intelligence, pp. 675-679. IOS Press, Amsterdam (2002)
7. Park, J.D., Darwiche, A.: Complexity results and approximation settings for MAP explanations. Journal of Artificial Intelligence Research 21, 101-133 (2004)
8. Charniak, E., Shimony, S.E.: Cost-based abduction and MAP explanation. Acta Informatica 66, 345-374 (1994)
9. Abdelbar, A., Hedetniemi, S.: Approximating MAPs for belief networks is NP-hard and other theorems. Artificial Intelligence 102, 21-38 (1998)
10. Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs. Springer, New York (2007)
11. Papadimitriou, C.H.: On the complexity of unique solutions. Journal of the ACM 31, 392-400 (1984)
12. Krentel, M.W.: The complexity of optimization problems. Journal of Computer and System Sciences 36, 490-509 (1988)
13. Toda, S.: Simple characterizations of P(#P) and complete problems. Journal of Computer and System Sciences 49, 1-17 (1994)
14. Kwisthout, J.H.P.: The Computational Complexity of Probabilistic Networks. PhD thesis, Universiteit Utrecht (2009)
15. Papadimitriou, C.H.: Computational Complexity. Addison-Wesley, Reading (1994)