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 \([a,b]\).