Our main procedure is based on the principle of indifference combined with the maximum entropy principle, thus keeping external model assumptions to a minimum.
Here we derive the probability measure and amplitude from the maximum entropy principle (Principle 2).
In the 'Maximum entropy results' section, maximum entropy principle is discussed to find the queue size distribution.
Thus, we have proved that the maximum entropy principle (Principle 2) and Cartan principle (Principle 1) are mathematically equivalent in a general dynamic space.
The difference was argued to originate from human neuropsychology and the maximum entropy principle.
Among all stationary distributions those satisfying a further requirement, the Gibbsian maximum entropy principle, play a special role.
The Gibbsian maximum entropy principle then requires that SG be maximal, given the constraints that are imposed on the system.
The key idea is to implement the maximum entropy principle in estimating the failure probability function.
The first realizes the maximum entropy principle to guide the selection of experiments in the context of Boolean networks learning.
