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Entropy $H(X)$ is formally defined as the expected surprise of a random variable $X$. It is calculated using the formula $H(X) = - \sum_{x} p(x) \log_2 p(x)$, where $p(x)$ is the probability mass function of $X$.

Conditions: $X$ is a discrete random variable.; The summation runs over all possible values of $x$.; $p(x)$ is the probability mass function, satisfying $p(x) \ge 0$ and $\sum_x p(x) = 1$.; Using logarithm base two yields entropy measured in bits.

Entropy $H(X)$ is formally defined as the expected surprise of a random variable $X$. It is calculated using the formula $H(X) = - \sum_{x} p(x) \log_2 p(x)$, where $p(x)$ is the probability mass function of $X$.

Conditions: $X$ is a discrete random variable.; The summation runs over all possible values of $x$.; $p(x)$ is the probability mass function, satisfying $p(x) \ge 0$ and $\sum_x p(x) = 1$.; Using logarithm base two yields entropy measured in bits.