7. List of References

[Bourinet2009]

J.-M. Bourinet, C. Mattrand, and V Dubourg. A review of recent features and improvements added to FERUM software. In Proc. of the 10th International Conference on Structural Safety and Reliability (ICOSSAR’09), Osaka, Japan, 2009.

[Bourinet2010]

J.-M. Bourinet. FERUM 4.1 User’s Guide, 2010.

[DerKiureghian2006]

Der Kiureghian, T. Haukaas, and K. Fujimura. Structural reliability software at the University of California, Berkeley. Structural Safety, 28(1-2):44–67, 2006.

[Hackl2013]

Hackl. Generic Framework for Stochastic Modeling of Reinforced Concrete Deterioration Caused by Corrosion. Master’s thesis, Norwegian University of Science and Technology, Trondheim, Norway, 2013.

[Langtangen2009]

Hans Petter Langtangen. Python Scripting for Computational Science. Springer-Verlag, 2009.

[Lutz2007]

Lutz. Learning Python. O’Reilly, 2007.

[Jensen2007]

Jensen, Finn V. and Thomas D. Nielsen (2007). Bayesian Networks and Decision Graphs. second edition. Information Science and Statistic. Springer Publishing Company, Incorporated. isbn: 9780387682815.

[Jordan2007]

Jordan, Michael I. (2007). An Introduction to Graphical Models. In preperation.

[Pearl1988]

Pearl, Judea (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Representation and Reasoning Series. San Francisco: Morgan Kaufman Publischer. isbn: 9781558604797.

[Pearl2000]

Pearl, Judea (2000). Causality: Models, Reasoning, and Inference. Cambridge, Massachusetts: Cambridge University Press. isbn: 9780521773621.

[Pernkopf2013]

Pernkopf, Franz, Robert Peharz, and Sebastian Tschiatschek (2013). “Introduction to Probabilistic Graphical Models”. In: E-Reference Signal Processin. In preperation. Elsevier.

[Stephenson2000]

Stephenson, Todd A. (Feb. 2000). “An Introduction to Bayesian Network Theory and Usage””. In: IDIAP Reseach Report 00-03.

[Koller2009]

Koller, Daphne and Nir Friedman (2009). Probabilistic Graphical Models: Principles and Techniques. first edition. Massachusetts: MIT Press.

[Murphy2012]

Murphy, Kevin P. (2012). Machine Learning: A Probabilistic Perspective. first edition. Adaptive computation and machine learning series. Cambridge, Massachusetts: MIT Press. isbn: 9780262018029.