کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
468189 698196 2013 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Maxallent: Maximizers of all entropies and uncertainty of uncertainty
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
Maxallent: Maximizers of all entropies and uncertainty of uncertainty
چکیده انگلیسی

The entropy maximum approach (Maxent) was developed as a minimization of the subjective uncertainty measured by the Boltzmann–Gibbs–Shannon entropy. Many new entropies have been invented in the second half of the 20th century. Now there exists a rich choice of entropies for fitting needs. This diversity of entropies gave rise to a Maxent “anarchism”. The Maxent approach is now the conditional maximization of an appropriate entropy for the evaluation of the probability distribution when our information is partial and incomplete. The rich choice of non-classical entropies causes a new problem: which entropy is better for a given class of applications? We understand entropy as a measure of uncertainty which increases in Markov processes. In this work, we describe the most general ordering of the distribution space, with respect to which all continuous-time Markov processes are monotonic (the Markov order). For inference, this approach results in a set of conditionally “most random” distributions. Each distribution from this set is a maximizer of its own entropy. This “uncertainty of uncertainty” is unavoidable in the analysis of non-equilibrium systems. Surprisingly, the constructive description of this set of maximizers is possible. Two decomposition theorems for Markov processes provide a tool for this description.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Mathematics with Applications - Volume 65, Issue 10, May 2013, Pages 1438–1456
نویسندگان
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