Shannon Entropy Calculator Online

Explanation

Shannon entropy, or more formally information entropy, is a key idea in information theory, a branch of research that is concerned with quantifying the information used in communication. Entropy often refers to the disorder or uncertainty within a system in thermodynamics and other sciences. Information entropy was first proposed by Claude Shannon in the late 1940s, and today's definition is practically identical to that of thermodynamics. The idea of information entropy is now used in a variety of contexts, including security, encryption, and even artificial intelligence and machine learning. Information entropy is defined mathematically as follows:

Shannon Entropy Calculator Formula:

How to use Shannon Entropy Calculator?

• You just need to enter the value of Probabilities in the input field.
• Seperate each probability with comma and each value must be in between 0 and 1.
• The result will automatically appear below.

More Calculation Examples (Shannon Entropy Examples)

• probabilities: 0.1, 0.5, 0.7, 0.4
• H(X) = 1.721


• probabilities: 0.2, 0.4, 0.6, 0.5
• H(X) = 1.935


• probabilities: 0.7,0.1,0.3,0.2
• H(X) = 1.678


• probabilities: 0.7,0.1,0.3,0.2,0.8
• H(X) = 1.935