Applying the Principle of Maximum Entropy in Bayesian Prior Distribution Assignment

Under the prior information that upper and lower bounds of the random quantity are symmetric with respect to the best estimate, this paper analyses the Bayesian prior distribution assignment using the principle of maximum entropy. With the exact lower and upper bounds, it approves uniform for the probability density function of the quantity and it has a curvilinear trapezoidal form for the inexact lower and upper bounds.