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Tribhuvan university > CSIT - 2065 Syllabus > Introduction to Artificial Intelligence > Old Questions > 2069

Introduction to Artificial Intelligence - Old Questions

Syllabus Notes Old Questions Unit Wise Questions Question Bank

7.  What do you mean by casual network? Explain it with practical application.

6 marks | Asked in 2069

Causal Networks / Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations.

  • Causal Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
  • Nodes in the graph represent the random variables and the directed edges between nodes represent conditional dependencies.
  • The edge exists between nodes if there exists conditional probability i.e. a link from X to Y means Y is dependent of X.
  • Each nodes are labelled with probability.

E.g.

P(x) = 0.5

P(y/x) = 0.7

P(z) = 0.8

P(u/y) = 0.47


Applications of Causal / Bayesian Network:

1. Spam Filter: You must be lying if you say that you’ve never wondered how Gmail filters spam emails (unwanted and unsolicited emails. It uses Bayesian spam filter, which is the most robust filter.

2. Turbo Code: Bayesian Networks are used to create turbo codes that are high-performance forward error correction codes. These are used in 3G and 4G mobile networks.

3. Image Processing: Bayesian Networks use mathematical operations to convert images into digital format. It also allows image enhancement. 

4. Biomonitoring: Quantifying the concentration of chemicals couldn’t get any easier than with Bayesian Networks. In this, the amount of blood and tissue in humans is measured using indicators.

5. Gene Regulatory Network (GNR): A GNR contains various DNA segments of a cell that interact with other cell contents through protein and RNA expression products. The predictions of its behavior can be analyzed using Bayesian Networks.