# Is it incorrect to call the probability mass function by the name “discrete probability density function”?

Commonly, the probability density function (pdf) is used when dealing with continuous random variables, while the probability mass function (pmf) is used for discrete random variables.

This also explains why they are called 'density function' and 'mass function' respectively.

However, my professor would talk about a 'discrete pdf' and a 'continuous pdf' instead of a pms and a pdf.

Wikipedia says that the probability density function is usually reserved for continuous variables.

It seems that my professor is not the only one to use the term discrete probability density function. It is also used here.

Is it incorrect to call the probability mass function by the name "discrete probability density function"?

(I would say so because the pmf does not describe a 'density' in the way that the pdf does.)

• Sorry but I fail to understand the question: if your teacher is precise about the meaning of "discrete pdf" (and it seems they are), what is the problem? If you want to be extra rigorous note that the PMF is a density, only with respect to the counting measure instead of the Lebesgue measure. – Did Jun 22 '16 at 12:41
• @Did This comment answered my question. Since it is a density with respect to the counting measure, then it's justifiable to call it a density function – hb20007 Jun 27 '16 at 13:06

Let $X$ be a discrete random variable with probability mass function $p_X : \mathcal{X} \to [0,1]$, where $\mathcal{X}$ is a discrete set (possibly countably infinite). Random variable $X$ can be thought of as a continuous random variable with the following probability density function
$$f_X (x) = \sum_{x_k \in \mathcal{X}} p_X (x_k) \, \delta (x - x_k)$$
where $\delta$ is the Dirac delta. One could call $f_X$ a discrete probability density function, as its support is a discrete set. However, do note that $f_X$ is a generalized function, not a proper function.
• @Did If you want to compute the PDF of $Z = X + Y$, where $X,Y$ are independent, $X$ is discrete and $Y$ is continuous, then the PDF of $Z$ is the convolution of $f_X$ and $f_Y$, which is $$f_Z (z) = \sum_{x_k \in \mathcal{X}} p_X (x_k) \, f_Y (z - x_k)$$ – Rodrigo de Azevedo Jun 22 '16 at 13:55