Transport property of multi-band topological material PtBi$$_2$$ studied by maximum entropy mobility spectrum analysis (MEMSA)

AbstractElectrical transport of both longitudinal and transverse directions carries rich information. Mobility spectrum analysis (MSA) is capable of extracting charge information from conductivity tensor, including charge types, concentration and mobilities. Using a numerical method based on maximum entropy principle, i.e., maximum entropy mobility spectrum analysis (MEMSA), mobility spectrum for $$\beta $$ β -type PtBi$$_2$$ 2 is studied. Three hole-pockets and two electron-pockets were found, including a small hole pocket with very high mobility, which is very likely corresponding to Dirac Fermions. Benefiting from our high resolution result, we studied temperature dependence of carrier properties and explained the sign change phenomenon of Hall conductivity. We further compared the results with band structure obtained by our first principle calculation. The present results prove MEMSA is a useful tool of extracting carries’ information in recently discovered Iron-based superconductors, and topological materials.

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Using the Maximum Entropy Principle as a Unifying Theory for Characterization and Sampling of Multi-scaling Processes in Hydrometeorology

10.21236/ada519510 ◽

2010 ◽

Author(s):

Rafael L. Bras ◽

Jingfeng Wang

Keyword(s):

Maximum Entropy ◽

Maximum Entropy Principle ◽

Entropy Principle

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An entropy concentration theorem: applications in artificial intelligence and descriptive statistics

Journal of Applied Probability ◽

10.2307/3214649 ◽

1990 ◽

Vol 27 (2) ◽

pp. 303-313 ◽

Cited By ~ 13

Author(s):

Claudine Robert

Keyword(s):

Artificial Intelligence ◽

Maximum Entropy ◽

Large Deviation ◽

Maximum Entropy Principle ◽

Statistical Modelling ◽

Linear Constraints ◽

Descriptive Statistics ◽

Entropy Principle ◽

Maximum Entropy Distribution ◽

Mathematical Justification

The maximum entropy principle is used to model uncertainty by a maximum entropy distribution, subject to some appropriate linear constraints. We give an entropy concentration theorem (whose demonstration is based on large deviation techniques) which is a mathematical justification of this statistical modelling principle. Then we indicate how it can be used in artificial intelligence, and how relevant prior knowledge is provided by some classical descriptive statistical methods. It appears furthermore that the maximum entropy principle yields to a natural binding between descriptive methods and some statistical structures.

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Random quantal fields and maximum entropy principle

10.1117/12.609473 ◽

2005 ◽

Author(s):

Maciej M. Duras

Keyword(s):

Maximum Entropy ◽

Maximum Entropy Principle ◽

Entropy Principle

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SINE ENTROPY FOR UNCERTAIN VARIABLES

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488513500359 ◽

2013 ◽

Vol 21 (05) ◽

pp. 743-753 ◽

Cited By ~ 19

Author(s):

KAI YAO ◽

JINWU GAO ◽

WEI DAI

Keyword(s):

Maximum Entropy ◽

Uncertainty Theory ◽

Maximum Entropy Principle ◽

Expected Value ◽

Uncertain Variables ◽

Entropy Principle

Entropy is a measure of the uncertainty associated with a variable whose value cannot be exactly predicated. In uncertainty theory, it has been quantified so far by logarithmic entropy. However, logarithmic entropy sometimes fails to measure the uncertainty. This paper will propose another type of entropy named sine entropy as a supplement, and explore its properties. After that, the maximum entropy principle will be introduced, and the arc-cosine distributed variables will be proved to have the maximum sine entropy with given expected value and variance.

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