Gönül Aycı
Jan 11, 2018 · 6 min read

Bayesian Networks (BNs)

Figure 1: Thomas Bayes (1701–1761) [1]
Equation 1: Bayes’ Theorem [4]
  1. The percent of people overall who are rich
  1. 5% of people are rich

Figure 2: Types of probabilistic relationships [5]
Figure 3: The graphical structure of the alarm example network with probability values.
  • Image Processing,
  • Spam Filters,
  • Semantic Search *,
  • Medical Diagnosis Systems,
  • Turbo Decoding Problem [6].

Implementation

Let’s create a simple Bayesian Networks with Jayes which is a Bayesian Network Library for Java [9]. Jayes is implemented by Michael Kutschke as his bachelor’s thesis.

Bonus

  • You can use a software to create Bayesian networks and make an analysis. Weka is a popular data mining software in Java which has various Bayesian network classifier learning algorithms [10].
Figure 4: Sample data
Figure 5: Using Bayes Net classifier and K2 algorithm
Figure 6: The probability values of first-feature node for each behavior type of information sources
Figure 7: p(A|B).p(B) = p(A, B) = p(B|A).p(A) [12]
Figure 8: Tree diagram for events A and B [12]

Further Readings

If you are interested in this topic, I would like to recommend some resources. Bold ones are more popular than others.

Notes

  • , ” denotes AND,
  • ¬ ” denotes NEGATION operator.

Acknowledgment

Many thanks to Burak Suyunu and Yusuf Hakan Kalaycı for their reviews. This post became more understandable after their comments.

References

[1] Wikipedia: Thomas Bayes

Gönül Aycı

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Ph.D. Candidate in CmpE @Boğaziçi University. 🏃‍♀️ 🚴‍♀️ 👩‍💻 👩‍🍳 🌳 📚 🎥 🎶

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