Artificial Intelligence - Old Questions
7. What do you mean by causal network? Explain it with practical application.
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.