Relative entropy, also known as Kullback-Leibler divergence, measures the difference between two probability distributions. It quantifies how much information is lost when one distribution is used to approximate another, thus providing insight into the efficiency of statistical models and the effectiveness of coding strategies. This concept connects closely with Shannon entropy, as both are foundational in understanding information content, while also playing a critical role in assessing mutual information and the dynamics of stochastic processes.
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