Sentence examples for maximum entropy to from inspiring English sources
Second, using the field data for calibration we applied the machine-learning method, maximum entropy, to predict the actual distribution of the sudden oak death epidemic.
Matter "just after" the Big Bang is usually posited to be in a state of maximum entropy – to be in thermal equilibrium.
Jain and Singh (2000) used the principle of maximum entropy to analyze the optimal flow control of a G/G/c finite capacity queue.
Further, Ke and Lin (2006) employed the principle of maximum entropy to derive the approximate formulas for the steady-state probability distributions of the queue length.
Jaynes attempts to save the principle of indifference and to extend the principle of maximum entropy to the continuous case, with his invariance condition: in two problems where we have the same knowledge, we should assign the same probabilities.
This paper researches on methods of the color image segmentation method of Lingwu long jujubes based on the maximum entropy to achieve the accuracy of image segmentation and improve accuracy of machine recognition.
In addition, we plan to apply maximum entropy methods to enable the dynamics of even poorly-sampled energy wells to be incorporated into the AR-model.
It is possible to use multiple maximum entropy method to segment the target objects correctly when more than two different regions need to be segmented in the image.
We estimate the functional relationships between regions of interest (ROIs) by fitting a pair-wise maximum entropy model to each ROI's pattern of allegiance to functional modules.
The proposed approach utilizes (i) polynomial correlated function expansion, a novel surrogate modelling technique, to determine the moments and (ii) maximum entropy method to determine an analytical expression for the response probability density function (PDF).
For biological information, most cases are like this with maximum entropy needed to be reduced and equal probability need to be assumed by organisms, so formula I=log(N) can be used to calculate the information.
