Incremental Junction Tree Inference

  • Hamza Agli
  • Philippe Bonnard
  • Christophe GonzalesEmail author
  • Pierre-Henri Wuillemin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 610)


Performing probabilistic inference in multi-target dynamic systems is a challenging task. When the system, its evidence and/or its targets evolve, most of the inference algorithms either recompute everything from scratch, even though incremental changes do not invalidate all the previous computations, or do not fully exploit incrementality to minimize computations. This incurs strong unnecessary overheads when the system under study is large. To alleviate this problem, we propose in this paper a new junction tree-based message-passing inference algorithm that, given a new query, minimizes computations by identifying precisely the set of messages that differ from the preceding computations. Experimental results highlight the efficiency of our approach.


Bayesian networks Incremental inference Junction tree 



This work was partially supported by IBM France Lab/ANRT CIFRE grant #2014/421.


  1. 1.
    Buchanan, B.G., Shortliffe, E.H.: Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading (1984)Google Scholar
  2. 2.
    Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks. Artif. Intell. 42(2–3), 393–405 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Dagum, P., Luby, M.: Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artif. Intell. 60(1), 141–153 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    D’Ambrosio, B.: Incremental probabilistic inference. In: Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 301–308 (1993)Google Scholar
  5. 5.
    Darwiche, A.: Dynamic join trees. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI), pp. 97–104 (1998)Google Scholar
  6. 6.
    Dean, T., Kanazawa, K.: A model for reasoning about persistence and causation. Comput. Intell. 5(2), 142–150 (1989)CrossRefGoogle Scholar
  7. 7.
    Flores, M.J., Gámez, J.A., Olesen, K.G.: Incremental compilation of Bayesian networks. In: Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI), pp. 233–240 (2003)Google Scholar
  8. 8.
    Heckerman, D.E., Shortliffe, E.H.: From certainty factors to belief networks. Artif. Intell. Med. 4(1), 35–52 (1992)CrossRefGoogle Scholar
  9. 9.
    Jensen, F., Lauritzen, S., Olesen, K.: Bayesian updating in causal probabilistic networks by local computations. Comput. Stat. Q. 4, 269–282 (1990)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  11. 11.
    Koller, D., Pfeffer, A.: Probabilistic frame-based systems. In: Proceedings of the 15th National Conference on Artificial Intelligence (AAAI), pp. 580–587 (1998)Google Scholar
  12. 12.
    Lauritzen, S., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their applications to expert systems. J. Roy. Stat. Soc. 50(2), 157–224 (1988)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Li, W., van Beek, P., Poupart, P.: Performing incremental Bayesian inference by dynamic model counting. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), pp. 1173–1179 (2006)Google Scholar
  14. 14.
    Lin, Y., Druzdzel, M.J.: Relevance-based sequential evidence processing in Bayesian networks. In: Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp. 446–450 (1998)Google Scholar
  15. 15.
    Madsen, A.L., Jensen, F.V.: Lazy propagation: a junction tree inference algorithm based on lazy evaluation. Artif. Intell. 113(12), 203–245 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Murphy, K.P.: Dynamic Bayesian networks: representation, inference and learning. Ph.D. thesis, UC Berkeley (2002)Google Scholar
  17. 17.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)zbMATHGoogle Scholar
  18. 18.
    Pfeffer, A.J.: Probabilistic reasoning for complex systems. Ph.D. thesis, Stanford University (2000)Google Scholar
  19. 19.
    Robinson, J., Hartemink, A.: Non-stationary dynamic Bayesian networks, pp. 1369–1376 (2009)Google Scholar
  20. 20.
    Shenoy, P., Shafer, G.: Axioms for probability and belief-function propagation. In: Proceedings of the Conference Uncertainty in Artificial Intelligence, vol. 4, pp. 169–198 (1990)Google Scholar
  21. 21.
    Torti, L., Gonzales, C., Wuillemin, P.H.: Speeding-up structured probabilistic inference using pattern mining. Int. J. Approximate Reasoning 54(7), 900–918 (2013)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hamza Agli
    • 1
  • Philippe Bonnard
    • 1
  • Christophe Gonzales
    • 2
    Email author
  • Pierre-Henri Wuillemin
    • 2
  1. 1.IBM France LabGentillyFrance
  2. 2.Sorbonne Universités, UPMC Univ Paris 6, CNRS, UMR 7606 LIP6ParisFrance

Personalised recommendations