Shannon Entropy Calculator

Overview

The Shannon Entropy Calculator computes the entropy of data sources - a measure of information content and uncertainty. Enter probability distributions or text and see entropy calculations. Includes joint entropy, conditional entropy, and mutual information. Essential for understanding information theory fundamentals.

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Tips

• Entropy measures average information content (bits per symbol)
• Higher entropy = more uncertainty/randomness
• Uniform distribution has maximum entropy
• Entropy H(X) = -Σ p(x) log₂ p(x)
• Joint entropy H(X,Y) for two variables
• Conditional entropy H(X|Y) = uncertainty in X given Y
• Mutual information I(X;Y) = shared information
• Applications: compression, cryptography, machine learning