3. Bibliography

CGH97

E. Castillo, J.M. Gutierrez, and A.S. Hadi. Expert Systems and Probabilistic Network Models. Springer, 1997.

Cow98

R.G. Cowell. Advanced inference in bayesian networks. In M.I. Jordan, editor, Learning in Graphical Models. A Bradford Book, 1998.

GDH22

H. Geffner, R. Dechter, and J.Y. Halpern, editors. Probabilistic and Causal Inference. ACM, 2022.

Hen88

M. Henrion. Propagating uncertainty in bayesian networks by probabilistic logic sampling. Uncertainty in Artificial Intelligence, 1988.

HD99

C. Huang and A. Darwiche. Inference in belief networks: a procedural guide. International Journal of Approximate Reasoning, 1999.

IC02

J.S. Ide and F.G. Cozman. Random generation of bayesian network. Advances in Artificial Intelligence, 2002.

Kol09

D. Koller. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.

Mur12

K.P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, 2012.

Pea88

J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.

Pea00

J. Pearl. Causality: Models, Reasoning and Inference. Cambridge University Press, 2000.

Pea18

J. Pearl. The Book of Why: The New Science of Cause and Effect. Basic Books, 2018.

PGJ16

J. Pearl, M. Glymour, and N.P. Jewell. Causal Inference in Statistics, A Primer. Wiley, 2016.