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Application of the maximum relative entropy method to the physics of ferromagnetic materials

Author

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  • Giffin, Adom
  • Cafaro, Carlo
  • Ali, Sean Alan

Abstract

It is known that the Maximum relative Entropy (MrE) method can be used to both update and approximate probability distributions functions in statistical inference problems. In this manuscript, we apply the MrE method to infer magnetic properties of ferromagnetic materials. In addition to comparing our approach to more traditional methodologies based upon the Ising model and Mean Field Theory, we also test the effectiveness of the MrE method on conventionally unexplored ferromagnetic materials with defects.

Suggested Citation

  • Giffin, Adom & Cafaro, Carlo & Ali, Sean Alan, 2016. "Application of the maximum relative entropy method to the physics of ferromagnetic materials," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 455(C), pages 11-26.
    • Handle: RePEc:eee:phsmap:v:455:y:2016:i:c:p:11-26
    DOI: 10.1016/j.physa.2016.02.069
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    References listed on IDEAS

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    1. Tseng, Chih-Yuan & Caticha, Ariel, 2008. "Using relative entropy to find optimal approximations: An application to simple fluids," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6759-6770.
    2. Giffin, Adom, 2009. "From physics to economics: An econometric example using maximum relative entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1610-1620.
    3. Cafaro, C. & Ali, S.A., 2008. "Can chaotic quantum energy levels statistics be characterized using information geometry and inference methods?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6876-6894.
    4. Adom Giffin, 2009. "From Physics to Economics: An Econometric Example Using Maximum Relative Entropy," Papers 0901.0401, arXiv.org.
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    Cited by:

    1. Zhou, Daoqing & He, Jingjing & Du, Yi-Mu & Sun, C.P. & Guan, Xuefei, 2021. "Probabilistic information fusion with point, moment and interval data in reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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