Question

Which among the following is not represented by node in Bayesian network?

(a) Latent variables

(b) Observable variables

(c) Hypotheses

(d) Conditions

The question was posed to me by my college director while I was bunking the class.

This intriguing question originated from Techniques in division Cognitive Techniques of Cognitive Radio

Answer 1

The correct answer is:

(d) Conditions

In a Bayesian network, the nodes represent variables, and the edges represent probabilistic dependencies between these variables. Here's a breakdown of the options:

1. Latent variables: These are unobserved or hidden variables in the system. They are commonly represented as nodes in a Bayesian network. For example, a latent variable could represent an underlying factor that influences other observable variables but is not directly measured.

2. Observable variables: These are the variables that can be directly measured or observed. They are also represented as nodes in the Bayesian network. Observable variables are typically the data that is used to infer the values of latent variables.

3. Hypotheses: In Bayesian networks, hypotheses can be modeled as nodes, especially when we are trying to infer certain outcomes or states based on evidence or prior knowledge. Hypotheses could represent assumptions or beliefs about the system being modeled.

4. Conditions: Conditions are typically not represented by nodes in a Bayesian network. Instead, conditions are usually modeled as the relationships (dependencies) between variables (i.e., the edges). In other words, conditions are expressed through the structure of the network, not as individual nodes.

So, conditions are not represented by a node in a Bayesian network. They are more closely related to the edges or the conditional dependencies between the nodes.

Therefore, the correct answer is (d) Conditions.