Semi-inclusive rapidity distributions from entropy maximization

1988 ◽  
Vol 38 (7) ◽  
pp. 2288-2290 ◽  
Author(s):  
Fujio Takagi ◽  
Tatsuo Tsukamoto
Keyword(s):  
Entropy Maximization ◽  
Rapidity Distributions

Parameter estimation of generalized backscatter models using entropy maximization

2003 ◽  
Author(s):  
Renata Smolikova ◽  
Mark P. Wachowiak ◽  
Jacek M. Zurada
Keyword(s):  
Parameter Estimation ◽  
Entropy Maximization

scholarly journals SELF-ORGANIZATION IN COMPLEX SYSTEMS AS DECISION MAKING

2014 ◽  
Vol 17 (03n04) ◽  
pp. 1450016 ◽  
Author(s):  
V. I. YUKALOV ◽  
D. SORNETTE
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Mathematical Formulation ◽  
Conditional Entropy ◽  
Self Organization ◽  
Entropy Maximization ◽  
Natural Systems ◽  
Macroscopic States ◽  
System States ◽  
Statistical Systems

The idea is advanced that self-organization in complex systems can be treated as decision making (as it is performed by humans) and, vice versa, decision making is nothing but a kind of self-organization in the decision maker nervous systems. A mathematical formulation is suggested based on the definition of probabilities of system states, whose particular cases characterize the probabilities of structures, patterns, scenarios, or prospects. In this general framework, it is shown that the mathematical structures of self-organization and of decision making are identical. This makes it clear how self-organization can be seen as an endogenous decision making process and, reciprocally, decision making occurs via an endogenous self-organization. The approach is illustrated by phase transitions in large statistical systems, crossovers in small statistical systems, evolutions and revolutions in social and biological systems, structural self-organization in dynamical systems, and by the probabilistic formulation of classical and behavioral decision theories. In all these cases, self-organization is described as the process of evaluating the probabilities of macroscopic states or prospects in the search for a state with the largest probability. The general way of deriving the probability measure for classical systems is the principle of minimal information, that is, the conditional entropy maximization under given constraints. Behavioral biases of decision makers can be characterized in the same way as analogous to quantum fluctuations in natural systems.

On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

2019 ◽  
Vol 17 (01) ◽  
pp. 1950003
Author(s):  
Apurba Roy ◽  
Santi P. Maity
Keyword(s):  
Image Reconstruction ◽  
Linear Prediction ◽  
Random Noise ◽  
Curvelet Transform ◽  
Conditional Entropy ◽  
Mr Images ◽  
Recursive Filtering ◽  
Entropy Maximization ◽  
Residual Noise ◽  
Incomplete Measurement

In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platform. Image reconstruction is done from incomplete measurement space with random noise injection iteratively. A weighted linear prediction is done for the unobserved space followed by spatial domain denoising through adaptive recursive filtering. The reconstructed images, however, suffer from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform (CT) is purposely used for removal of noise and for edge enhancement through hard thresholding and suppression of approximate subbands, respectively. Then a fuzzy entropy-based clustering, using genetic algorithms (GAs), is done for segmentation of sharpen MR Image. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation of the reconstructed images along with relative gain over the existing works.

Electron microscopy at 1-Å resolution by entropy maximization and likelihood ranking

Nature ◽  
1992 ◽  
Vol 355 (6361) ◽  
pp. 605-609 ◽  
Author(s):  
W. Dong ◽  
T. Baird ◽  
J. R. Fryer ◽  
C. J. Gilmore ◽  
D. D. MacNicol ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Entropy Maximization

scholarly journals On the way towards a generalized entropy maximization procedure

2009 ◽  
Vol 373 (36) ◽  
pp. 3230-3234 ◽  
Author(s):  
G. Baris Bagci ◽  
Ugur Tirnakli
Keyword(s):  
Entropy Maximization ◽  
Generalized Entropy ◽  
The Way

scholarly journals Entropy Balancing is Doubly Robust

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Qingyuan Zhao ◽  
Daniel Percival
Keyword(s):  
Propensity Score ◽  
Observational Studies ◽  
Optimization Problem ◽  
Entropy Maximization ◽  
Entropy Balancing ◽  
Covariate Balance ◽  
Original Proposal ◽  
Variance Bound ◽  
Doubly Robust ◽  
Theoretical Results

AbstractCovariate balance is a conventional key diagnostic for methods estimating causal effects from observational studies. Recently, there is an emerging interest in directly incorporating covariate balance in the estimation. We study a recently proposed entropy maximization method called Entropy Balancing (EB), which exactly matches the covariate moments for the different experimental groups in its optimization problem. We show EB is doubly robust with respect to linear outcome regression and logistic propensity score regression, and it reaches the asymptotic semiparametric variance bound when both regressions are correctly specified. This is surprising to us because there is no attempt to model the outcome or the treatment assignment in the original proposal of EB. Our theoretical results and simulations suggest that EB is a very appealing alternative to the conventional weighting estimators that estimate the propensity score by maximum likelihood.

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