Maximum Entropy Principle in statistical inference: case for non-Shannonian entropies - CSH

Maximum Entropy Principle in statistical inference: case for non-Shannonian entropies


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In this Letter, we show that the Shore-Johnson axioms for the maximum entropy principle in statistical estimation theory account for a considerably wider class of entropic functional than previously thought. Apart from a formal side of the proof where a one-parameter class of admissible entropies is identified, we substantiate our point by analyzing the effect of weak correlations and by discussing two pertinent examples: two-qubit quantum system and transverse-momentum behavior of hadrons in high-energy proton-proton collisions.

 

J. Krobel, P. Jizba, Maximum Entropy Principle in statistical inference: case for non-Shannonian entropies, Physical Review Letters 122 (2019) 120601