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Entropy stands as a central pillar uniting classical thermodynamics, statistical mechanics, and modern information theory. Coined initially to measure the irreversible dissipation of thermal energy within closed mechanical systems, entropy has evolved into a comprehensive metric quantifying microscopic uncertainty, disorder, and missing information. When analyzing discrete probability distributions, calculating entropy allows researchers to determine how uniformly states are populated across phase space. High entropy values imply extreme uncertainty and widespread dispersal among available microstates, whereas low entropy points to localized, predictable configurations.
The standard quantitative expression implemented within this script relies on the foundational logarithmic relationship established by Ludwig Boltzmann, Willard Gibbs, and later refined by Claude Shannon. For a discrete probability distribution comprising individual states with associated occurrence probabilities $p_i$, the mathematical formulation for entropy $S$ is expressed as:
$$S = -k \sum_{i=1}^{n} p_i \log_b(p_i)$$
In this equation, $p_i$ represents the normalized probability of finding the physical system in state $i$, subject to the normalization constraint $\sum p_i = 1$. The parameter $b$ defines the logarithmic base, shifting output units between nats, bits, or bans. The constant $k$ scales the measure relative to physical units, such as the Boltzmann constant $k_B$ in thermodynamics.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.