Journal of Numerical Analysis and Approximation Theory (Aug 2003)
Fisher's information measures and truncated normal distributions (II)
Abstract
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 \([a,b]\).