Can the Dirac delta function (or distribution) be a probability density function of a random variable. To my knowledge, it seem to satisfy the conditions. To my interpretation getting a positive real number as the outcome is 1 and that for a negative real number is zero. I wonder what could be the expected value. My question is, whether it is a valid probability density function of a random variable.
Probability Theory – Can a Dirac Delta Function Be a Probability Density Function?
probability theory
Best Answer
As explained in Gortaur's answer a delta function cannot be the probability density function of a real random variable.
Nevertheless sums of delta functions can be viewed as the "missing link" between discrete and continuous random variables / probability distributions, in the following way:
If $X$ is a discrete random variable taking values $x_k\in{\mathbb R}$ $\ (k\in I$, $\ I$ a countable index set) with probabilities $p_k$ then one can replace the probability space $I$ with the probability space ${\mathbb R}$, provided with the probability measure $$\mu\ :=\ \sum_{k\in I} p_k \ \delta_{x_k}\ ,$$ where $\delta_x$ denotes a unit point mass at the point $x$. In this way $X$ now has become a real random variable. If $f:\ {\mathbb R}\to {\mathbb R}$ is a reasonable function then the expectation $E\bigl(f(X)\bigr)$ may be written as an integral: $$E\bigl(f(X)\bigr)\ =\ \int_{-\infty}^\infty f(x)\ d\mu(x)\ .$$