Cyclic Bayesian Network : Markov Process Approach

• Autorzy: Kłopotek M. A.
• Języki publikacji: EN

Abstrakty

EN

The paper proposes a new interpretation of the concept of cyclic Bayesian Networks, based on stationary Markov processes over feature vector state transitions.

Słowa kluczowe

EN

Bayesian networks; Markov process

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2006
• Tom: Vol. 1(7)
• Strony: 47--55
• Opis fizyczny: Bibliogr. 8 poz., wykr.

Twórcy

autor

Kłopotek M. A.

• Institute of Computer Science, Polish Academy of Sciences ul. Ordona 21, 01-237 Warsaw Poland
• Institute of Computer Science, University of Podlasie, ul. Sienkiewicza 51, 08-110 Siedlce, Poland

Bibliografia

• 1. [Cooper:92] G.F. Cooper, E. Herskovits: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 (1992), 309-347.
• 2. [Geiger:90] D. Geiger, T. erma, J. Pearl: d-Separation: From theorems to algorithms. W: M.Henrion, R.D.Shachter, L.N.Kamal, J.F.Lemmer (eds): Uncertainty in Artificial Intelligence 5, Elsevier Science Publishers B.V. (North-Holland), 1990, 139-148.
• 3. [Ghahramani:98] Z. Ghahramani: Learning dynamic Bayesian networks. In C.L. Giles and M. Gori, editors, Adaptive Processing of Sequences and Data Structures, volume 1387 of Lecture Notes in Computer Science, pages 168¬-197. Springer, 1998.
• 4. [Guyader:01] Guyader A. Fabre, E. Guillemot, C. Robert, M.: Joint source-channel turbo decoding of entropy-coded sources, IEEE Journal on Selected Areas in Communications, Sep 2001, Volume: 19, Issue: 9, pp. 1680-1696 URL: www.uhb.fr/sc_sociales/ labstats/AGUYADER/doc/ieee-jsac.pdf
• 5. [Motwani:95] R. Motwani, P. Raghavan: Randomized Algorithms. Cambridge University Press, 1995.
• 6. [Pearl:88] J. Pearl: Probabilistic Reasoning in Intelligent Systems:Networks of Plausible Inference, Morgan Kaufmann, San Mateo CA, 1988.
• 7. [Spirtes:93] P. Spirtes, C. Glymour, R. Scheines: Causation, Prediction and Search, Lecture Notes in Statistics 81, Springer-Verlag, 1993.
• 8. [Zou:05] Min Zou, Suzanne D. Conzen: A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data Bioinformatics 2005 21(1): 71-79.