The sample space of binomial distribution is the set $\{0,1\}$ and its $\sigma$-algebra is the power set of $\{0,1\}$ while the sample space of normal distribution is $\mathbb R$ and its $\sigma$-algebra is the Lebesgue measurable set. In these cases, the probability measure of binomial distribution is of the form of exponential family while the radon-nikodym derivative of the probability measure (i.e. pdf) of normal distribution is of the form of exponential family. Their natures are so different and why do people even call it a family?

My questions are:

  1. How do we determine if exponential family gives us the probability measure or the probability density function?
  2. How can we determine the sample space, $\sigma$-algebra and its probability measure from an exponential family?

Any help are welcome.

  • 2
    $\begingroup$ The sample space of a Binomial $\mathcal B(n,p)$ is $\{0,1,\ldots,n\}$. $\endgroup$
    – Xi'an
    Jul 29, 2021 at 3:14

1 Answer 1


Some elements of answer:

  • The pmf for a distribution with a countable sample space is its density wrt to the counting measure.
  • Exponential families are made of families of parametric distributions whose density wrt to a given dominating measure $\text d\lambda(x)$ write as $f_\theta(x)=\exp\{R(\theta)^\text{T}S(x)-\psi(\theta)\}$, where $\psi(\theta)$ is the log-normalising constant.$^1$ The Binomial and the Normal distributions belong to different exponential families.$^2$
  • the sample space, $\mathfrak X$, its σ-algebra $\mathfrak B(\mathfrak X)$ and the dominating measure $\text d\lambda(x)$ are components of the definition of a given exponential family, just as much as the functions $R(\cdot)$ and $S(\cdot)$ above. If they are not provided, the associated exponential family is not defined.$^3$

$^1$I purposedly did not include the traditional $h(x)$ in the representation of $f_\theta(x)$, i.e., $f_\theta(x)=h(x)\exp\{R(\theta)^\text{T}S(x)-\psi(\theta)\}$ to stress the fact that $h(\cdot)$ can as well be part of the dominating measure. Changing $h(\cdot)$ thus amounts to changing the dominating measure.

$^2$In particular, the Binomial distribution is absolutely continuous wrt the counting measure on $\mathfrak X=\mathbb N$ and the Normal distribution is absolutely continuous wrt the Lebesgue measure on $\mathbb R$

$^3$In other words, a probability distribution does not exist independently of its dominating measure, which itself does not exist independently of its σ-algebra $\mathfrak B(\mathfrak X)$, which itself requires a sample space $\mathfrak X$


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