As best we can tell from cosmological data, our universe started out with maximum entropy, meaning minimum information and minimum complexity.
Several designs are close to having maximum entropy, which means that the designs are robust.
This assumption is translated into the requirement that each π i(a) is the distribution of maximum entropy with mean values given by Eq. (5).
This distribution is characterized by maximum entropy for specified mean and variance, and integer support on.
The purpose of this study is to develop and quantitatively assess whether fusion of EEG and MEG (MEEG) data within the maximum entropy on the mean (MEM) framework increases the spatial accuracy of source localization, by yielding better recovery of the spatial extent and propagation pathway of the underlying generators of inter-ictal epileptic discharges (IEDs).
However, like viruses, in order to change protein expressions flexibly, organisms may use an information mechanism that can produce protein molecules one by one in sequence to reduce the maximum entropy to 0, which means each protein or operon has at least one regulatory sequence.
Kemp (2006) presented a discrete half normal distribution as a maximum entropy distribution for given mean and variance with support 0, 1, 2 ⋯.
Very few papers report on recombination rate distributions calculated by means of inverse Laplace transform [7] for porous silicon or by means of the maximum entropy method for Si-NCs produced by laser pyrolysis of silane [11].
Conventional, soft-partition clustering approaches, such as fuzzy c-means (FCM), maximum entropy clustering (MEC) and fuzzy clustering by quadratic regularization (FC-QR), are usually incompetent in those situations where the data are quite insufficient or much polluted by underlying noise or outliers.
By means of Bayesian Maximum Entropy (BME) analysis, physically meaningful and sufficiently detailed space time maps of the seasonal O3 patterns are generated across space and time.
