Paper award 36864
IEEE Transactions on Information Theory
Paper award 36863
IEEE Transactions on Information Theory
Paper award 36843
IEEE Transactions on Information Theory
Paper award 36842
IEEE Transactions on Information Theory
Basic Notions
- Entropy
- Differential entropy
- Graph entropy
- Conditional entropy
- Mutual info
Entropy
Definitions
Let \(X\) be a discrete random variable defined on a finite alphabet \(\mathcal{X}\) and with probability mass function \(p_X\). The entropy of \(X\) is the random variable \(H(X)\) defined by
\[ H(X)=\log\frac{1}{p_X(X)}.\]
The base of the logarithm defines the unit of entropy. If the logarithm is to the base 2, the unit of entropy is the bit. If the if the logarithm is to the base \(e\), the unit of entropy is the nat.