Joint entropy in information theory

The joint entropy is an entropy measure used in information theory. The joint entropy measures how much entropy is contained in a joint system of two random variables. If the random variables are X and Y, the joint entropy is written H(X,Y).

1. What is joint and conditional entropy?
2. What is entropy used for in information theory?
3. What are the types of entropy in information theory?
4. What is joint probability in information theory?

What is joint and conditional entropy?

joint entropy is the amount of information in two (or more) random variables; conditional entropy is the amount of information in one random variable given we already know the other.

What is entropy used for in information theory?

Information provides a way to quantify the amount of surprise for an event measured in bits. Entropy provides a measure of the average amount of information needed to represent an event drawn from a probability distribution for a random variable.

What are the types of entropy in information theory?

There are two types of Entropy:

Joint Entropy. Conditional Entropy.

What is joint probability in information theory?

Joint probability is a statistical measure that calculates the likelihood of two events occurring together and at the same point in time. Joint probability is the probability of event Y occurring at the same time that event X occurs.