We have presented a new axiomatic derivation of Shannon entropy for a discrete probability distribution on the basis of the postulates of additivity and concavity of the entropy function. We have then modified Shannon entropy to take account of observational uncertainty.The modified entropy reduces, in the limiting case, to the form of Shannon differential entropy. As an application, we have de...

in this paper, the ambiguity of nite state irreducible markov chain trajectories is reminded and is obtained for two state markov chain. i give an applicable example of this concept in president election

the aim of this study is to show that the measures derived from electrocardiogram (ecg) signals many a time perform better than the same measures obtained from heart rate (hr) signals. a comparison was made to investigate how far the nonlinear symbolic dynamics approach helps to characterize the nonlinear properties of ecg signals and hr signals, and thereby discriminate between normal and cong...

The Rényi entropy is a generalization of Shannon entropy to a one-parameter family of entropies. Tsallis entropy too is a generalization of Shannon entropy. The measure for Tsallis entropy is non-logarithmic. After the introduction of Shannon entropy , the conditional Shannon entropy was derived and its properties became known. Also, for Tsallis entropy, the conditional entropy was introduced a...

a review of damages caused by landslide shows the need for studying effective parameters in landslide occurrence and prediction. this study aims to improve landslide prediction results for tutkabon region in gilan province. to this end, shannon entropy theory was employed for modeling and considering data uncertainty. slope, height, geomorphologic conditions, earth’s curve, closeness to river, ...

In this paper, we show that in order to obtain the Tsallis entropy rate for stochastic processes, we can use the limit of conditional entropy, as it was done for the case of Shannon and Renyi entropy rates. Using that we can obtain Tsallis entropy rate for stationary Gaussian processes. Finally, we derive the relation between Renyi, Shannon and Tsallis entropy rates for stationary Gaussian proc...

We analyze the performance of the top-down multiclass classification algorithm for decision tree learning called LOMtree, recently proposed in the literature Choromanska and Langford (2014) for solving efficiently classification problems with very large number of classes. The algorithm online optimizes the objective function which simultaneously controls the depth of the tree and its statistica...