Fisher's information measures and truncated normal distributions (II) - Ion Mihoc; Cristina Ioana Fătu (Journal of Numerical Analysis and Approximation Theory, 2003)
The aim of this paper is to give some properties for the Fisher information measure when a random variable \(X\) follows a truncated probability distribution. A truncated probability distribution can be regarded as a conditional probability distribution, in the sense that if \(X\) has an unrestricted distribution with the probability density function \(f(x), \) then \(f_{a\leftrightarrow b}(x)\) is the probability density function which governs the behavior of \(X\), subject to the condition that \(X\) is known to lie in \([a,b]\).