Let $X\sim \operatorname{Poi}[\lambda]$ such that $\lambda\in\mathbb{N}$.


Can this be explained on an intuitive level?


2 Answers 2


Although the result is easy to verify when one is given a formula for a Poisson distribution, that provides little for the intuition to work with. Instead, what we need is to develop an understanding of this distribution in terms of some probabilistic mechanism that we can see, experiment with, and understand easily: that would be genuinely intuitive.

The following answer repeats the original (and standard) development of the Poisson distribution as a limiting value of Binomial distributions, but instead of trying to obtain the probabilities directly (which requires some knowledge of the Gamma function), it focuses on a particularly simple relationship among the various probabilities. The desired result drops out immediately with no calculation at all.

Imagine a large number $n$ of bins and a coin with a chance $p$ of falling heads. Flip the coin independently for each bin, "filling" it when the coin falls heads and otherwise leaving it "empty." Suppose $k$ of the bins end up full as shown with the dark shading in the figure, and the remaining $n-k$ are empty.

Figure 1: Relations between bin counts

This is the situation depicted at the right (showing $k=4$ full bins). The situation at the left is another possible outcome with only $k-1$ full bins. It was created by choosing one of the $k$ bins at the right. The "$k,1-p$" in the top arrow reminds us of the two chances involved in relating the right to the left: pick one bin out of $k$ (with chance $1/k$ for each such bin) and change the coin's outcome (with chance $1-p$).

The bottom arrow shows what changes in moving from the left to the right: one of the $n-(k-1)$ empty bins is chosen and then filled by an outcome having probability $p$.

This shows how to relate the chance of filling $k$ bins, which I will write $\pi(k,n,p)$, to the chance of filling $k-1$ bins, $\pi(k-1,n,p)$:

$$ \pi(k,n,p)(k)(1-p) = \pi(k-1,n,p)(n-k+1)(p).\tag{1}$$

Suppose now that $n$ becomes arbitrarily large, but as it does, $\lambda = p n$ (the expected proportion of full bins) stays constant. Multiplying both sides by $n/k$ and dividing them by $n-k+1$ preserves the equality $(1)$, enabling us to rewrite it in terms of $\lambda$ with all the dependency on $n$ on one side:

$$\pi(k,n,n\lambda)\left(\frac{n-\lambda}{n-k+1}\right) = \pi(k-1,n,n\lambda)\left(\frac{\lambda}{k}\right).\tag{2}$$

For a fixed value of $k$, as $n$ grows the coefficient on the left of $(2)$ becomes uniformly close to $1$. Thus, to an excellent approximation (which is on the order of $1/n$), we may take

$$\pi(k,n,n\lambda) \approx \pi(k-1,n,n\lambda)\left(\frac{\lambda}{k}\right)\tag{3}$$

for this particular $k$ (and all smaller values, too).

This, of course, recapitulates the construction of the Poisson distribution as a limit of Binomial$(n, \lambda/n)$ distributions. We may now construct the full limiting distribution in terms of the chance of $k=0$ for the Poisson$(\lambda)$ distribution,

$$p_\lambda(0) = \lim_{n\to\infty}\pi(0,n,n\lambda).$$

According to $(3)$, this is multiplied by $\lambda/1$ to obtain $p_\lambda(1)$, and that is multiplied by $\lambda/2$ to obtain $p_\lambda(2)$, and so on. Because this works for any finite $k$, it works for all $k$.

Figure 2: Relations among Poisson probabilities

(The subscript $\lambda$ is dropped in the figure for brevity.)

For as long as $k \lt \lambda$, the chances keep increasing. Once $\lambda \gt k$, they decrease. Therefore the Poisson distribution is unimodal.

In some special cases, the mode can occur at two adjacent values: this is precisely when $\lambda/k = 1$, for then the two chances $p_\lambda(k-1)$ and $p_\lambda(k)$ are equal. Obviously this happens if and only if $\lambda$ is integral, in which case $k=\lambda$, QED.

Note that this is a stronger result than stated in the question, because it demonstrates the converse: when a Poisson distribution's mode occurs at two values, its rate $\lambda$ must be integral.

Incidentally, $p_\lambda(0)$--and therefore all the chances--is found by normalizing the sum

$$\sum_{k=0}^\infty p_\lambda(k) = p_\lambda(0)\left[1 + \frac{\lambda}{1} + \left(\frac{\lambda}{1}\frac{\lambda}{2}\right) + \cdots + \left(\frac{\lambda}{1}\frac{\lambda}{2}\cdots\frac{\lambda}{k}\right)+\cdots\right]$$

to unity. This sum is easily recognizable as $p_\lambda(0)\exp(\lambda)$, whence finally $p_\lambda(0) = 1/\exp(\lambda) = \exp(-\lambda)$ and therefore

$$p_\lambda(k) = \exp(-\lambda)\frac{\lambda^k}{1\cdot 2\cdots (k-1)\cdot k} = \frac{\lambda^k e^{-\lambda}}{k!}$$

for all $k \ge 0$.

  • $\begingroup$ Wonderful graphics! I'm not going to ask for the recipe... it would be impolite! $\endgroup$ May 9, 2016 at 14:32
  • 3
    $\begingroup$ @AntoniParellada The recipe is "Powerpoint." These graphics use a small number of basic elements that are copied and pasted, distributed, aligned, and grouped. $\endgroup$
    – whuber
    Nov 17, 2018 at 21:56

Recall the density function for a Poisson RV is $e^{-\lambda}\lambda^x / x!$, you plugged $\lambda$ and $\lambda-1$ in for $x$. It is simple arithmetic to show it's true and that's enough intuition for me. The intuition is that Poisson RVs, for $\lambda \ge 1$, have convex density. If one allowed $x$ to take non-integral values, the curve would show a mode at $\lambda-0.5$. Proving this is easy, but it's simple to connect this with the idea that once $x$ is "forced" to take integral values, there are two modes interpolating this values.

curve(exp(4)*4^x/gamma(x+1), from=0, to=10)

enter image description here

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    $\begingroup$ Thank you for your answer. The calculation alone does not satisfy me --- I have it above. The mode of $\frac{e^{-\lambda}\lambda^x}{\Gamma(x+1)}$ isn't at $x=\lambda-\frac12$... it's close but for e.g. $\lambda=4$ it isn't equal to $3.5$... see math.stackexchange.com/questions/246496/… $\endgroup$ May 2, 2016 at 16:45
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    $\begingroup$ The functional I am using is $\exp(-\lambda) \lambda^x / \gamma(x-1)$ is using $x$ continuously valued, so it is not a Poisson density. But it is a proper density function over positive reals. I only illustrate using this because the mode of this density is as stated. But if the support were moved from $\mathbb{R}$ to $\mathbb{N}$ the discrete RV mode would be "interpolating" the continuous one (which is nonintegral). $\endgroup$
    – AdamO
    May 2, 2016 at 17:01
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    $\begingroup$ It is as if you didn't read my comment not looked at the link..the mode of that continuous function is not exactly where you say it is. $\endgroup$ May 2, 2016 at 17:17
  • $\begingroup$ @JpMcCarthy I concede there may be some subtlety to your question. You should update to clarify what your understanding thus far is, especially regarding how you define a "mode" (on a discrete distribution, or a continuous one... is it measurable? unique? compact? what about a uniform or discrete uniform DF?). $\endgroup$
    – AdamO
    May 2, 2016 at 17:20
  • $\begingroup$ I understand your argument - if the mode of the continuous function is close to the mean less one half - the probabilities either side should be close to each other....I suppose my question now is why isn't the mode of the continuous function equal to lambda. $\endgroup$ May 2, 2016 at 17:21

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