Improving the Minimum Free Energy Principle to the Maximum Information Efficiency Principle by Chenguang Lu

“…Based on the study of the <i>R</i>(<i>G</i>) function and logical Bayesian Inference, this paper proposes the Semantic Variational Bayesian (SVB) and the Maximum Information Efficiency (MIE) principle. Theoretic analysis and computing experiments prove that <i>R</i> − <i>G</i> = <i>F</i> − <i>H</i>(<i>X</i>|<i>Y</i>) (where <i>F</i> denotes VFE, and <i>H</i>(<i>X</i>|<i>Y</i>) is Shannon conditional entropy) instead of <i>F</i> continues to decrease when optimizing latent variables; SVB is a reliable and straightforward approach for latent variables and active inference. …”
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A Semantic Generalization of Shannon’s Information Theory and Applications by Chenguang Lu

“…The paper also proposes refining Friston’s minimum free energy principle into the maximum information efficiency principle. Lastly, it compares G theory with other semantic information theories and discusses its limitation in representing the semantics of complex data.…”
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