The maximum entropy (ME) method is a recently-developed approach for estimating local false discovery rates (LFDR) that incorporates external information allowing assignment of a subset of tests to a category with a different prior probability of following the null hypothesis. Using this ME method, we have reanalyzed the findings from a recent large genome-wide association study of coronary art...

Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi and Tsallis entropies. This work has novelty based ...

Sanov’s Theorem and the Conditional Limit Theorem (CoLT) are established for a multicolor Pólya Eggenberger urn sampling scheme, giving the Pólya divergence and the Pólya extension to the Maximum Relative Entropy (MaxEnt) method. Pólya MaxEnt includes the standard MaxEnt as a special case. The universality of standard MaxEnt advocated by an axiomatic approach to inference for inverse problems i...

A new algorithm for computing the maximum entropy ranking (me-ranking) over a set of variable strength defaults is given. Although this requires more information than its predecessor in terms of strengths for defaults, it has a much wider applicability, and allows for greater expressiveness in encoding default knowledge. The algorithm is shown to be sound and complete to the extent that it alwa...

In this paper the information theoretic principle of Maximum Entropy (ME) is applied, as a method of inference, towards the study of the M/G/1 queueing model at equilibrium with either single or multiple server vacation periods under both exhaustive and non-exhaustive scheduling schemes. An analytic ME approximation for the characterisation of a universal form for the queue length distribution ...