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Maximum entropy PDF projection: A review

 
: Baggenstoss, P.M.

:

Verdoolaege, G. ; American Institute of Physics -AIP-, New York:
Bayesian inference and maximum entropy methods in science and engineering : Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2016), 10-15 July 2016, Ghent, Belgium
New York, N.Y.: AIP Press, 2017 (AIP Conference Proceedings 1853)
ISBN: 978-0-7354-1527-0
Art. 070001
International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt) <36, 2016, Ghent>
Englisch
Konferenzbeitrag
Fraunhofer FKIE ()

Abstract
We review maximum entropy (MaxEnt) PDF projection, a method with wide potential applications in statistical inference. The method constructs a sampling distribution for a high-dimensional vector x based on knowing the sampling distribution p(z) of a lower-dimensional feature z = T (x). Under mild conditions, the distribution p(x) having highest possible entropy among all distributions consistent with p(z) may be readily found. Furthermore, the MaxEnt p(x) may be sampled, making the approach useful in Monte Carlo methods. We review the theorem and present a case study in model order selection and classification for handwritten character recognition.

: http://publica.fraunhofer.de/dokumente/N-455796.html