Bayesian Network

From Ioannis Kourouklides

This page contains resources about Belief Networks and Bayesian Networks (directed graphical models), also called Bayes Networks.

Bayesian Networks do not necessarily follow Bayesian Methods, but they are named after Bayes' Rule. Bayesian and Non-Bayesian (Frequentist) Methods can either be used.

A distinction should be made between Models and Methods (which might be applied on or using these Models).

Subfields and Concepts[edit]

Online Courses[edit]

Video Lectures[edit]

Lecture Notes[edit]

Books and Book Chapters[edit]

  • Davidson-Pilon, C. (2015). Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional.
  • Conrady, S., & Jouffe, L. (2015). Bayesian Networks and BayesiaLab: A Practical Introduction for Researchers. BayesiaLab USA.
  • Koduvely, H. M. (2015). Learning Bayesian Models with R. Packt Publishing.
  • Theodoridis, S. (2015). "Section 15.3: Bayesian Networks and the Markov Condition". Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
  • Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis. Boca Raton, FL, USA: Chapman & Hall/CRC.
  • Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian Networks in R. Springer122, 125-127.
  • Barber, D. (2012). "Chapter 3: Belief Networks". Bayesian Reasoning and Machine Learning. Cambridge University Press.
  • Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern Classification. John Wiley & Sons.
  • Murphy, K. P. (2012). "Chapter 10: Directed graphical models (Bayes nets) ". Machine Learning: A Probabilistic Perspective. MIT Press.
  • Russell, S. J., & Norvig, P. (2010). "Part IV: Uncertain knowledge and reasoning". Artificial Intelligence: A Modern Approach. Prentice Hall.
  • Koller, D., & Friedman, N. (2009). "Chapter 3: The Bayesian Network Representation". Probabilistic Graphical Models. MIT Press.
  • Darwiche, A. (2009). Modeling and Reasoning with Bayesian Networks. Cambridge University Press.
  • Nielsen, T. D., & Jensen, F. V. (2007). Bayesian Networks and Decision Graphs. Springer Science & Business Media.
  • Bishop, C. M. (2006). "Section 8.1: Bayesian Networks". Pattern Recognition and Machine Learning. Springer.
  • Murphy, K. P. (2002). "Chapter: Dynamic Bayesian Networks". In M. Jordan, Probabilistic Graphical Models.
  • Murphy, K. P. (2002). "Dynamic Bayesian Networks: Representation, Inference and Learning". PhD Diss. University of California, Berkeley.
  • Friedman, N., Murphy, K., & Russell, S. (1998). Learning the structure of dynamic probabilistic networks. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (pp. 139-147). Morgan Kaufmann.
  • Ghahramani, Z. (1998). Learning dynamic Bayesian networks. In Giles, C. L., & Gori, M. (Eds.). Lecture Notes in Computer Science 1387. Adaptive processing of sequences and data structures, (pp. 168-197). Springer.
  • Mitchell, T. M. (1997). "Chapter 6: Bayesian Learning". Machine Learning. McGraw Hill.
  • Jensen, F. (1996). An Introduction to Bayesian Networks. Springer.

Scholarly Articles[edit]

  • Mnih, A., & Gregor, K. (2014). Neural variational inference and learning in belief networks. arXiv preprint arXiv:1402.0030.
  • Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine learning20(3), 197-243.



See Software and Software for complete lists.

See also[edit]

Other Resources[edit]