Negative binomial distribution

Different texts adopt slightly different definitions for the negative binomial distribution. They can be distinguished by whether the support starts at k = 0 or at k = r, whether p denotes the probability of a success or of a failure, and whether r represents success or failure,^[1] so it is crucial to identify the specific parametrization used in any given text.
Probability mass function The orange line represents the mean, which is equal to 10 in each of these plots; the green line shows the standard deviation.
Notation	$\mathrm{NB}(r,\,p)$
Parameters	r > 0 — number of failures until the experiment is stopped (integer, but the definition can also be extended to reals) p ∈ (0,1) — success probability in each experiment (real)
Support	k ∈ { 0, 1, 2, 3, … } — number of successes
pmf	${k+r-1 \choose k}\cdot (1-p)^r p^k,\!$ involving a binomial coefficient
CDF	$1-I_p(k+1,\,r),$ the regularized incomplete beta function
Mean	$\frac{pr}{1-p}$
Mode	$\begin{cases}\big\lfloor\frac{p(r-1)}{1-p}\big\rfloor & \text{if}\ r>1 \\ 0 & \text{if}\ r\leq 1\end{cases}$
Variance	$\frac{pr}{(1-p)^2}$
Skewness	$\frac{1+p}{\sqrt{pr}}$
Ex. kurtosis	$\frac{6}{r} + \frac{(1-p)^2}{pr}$
MGF	$\biggl(\frac{1-p}{1 - p e^t}\biggr)^{\!r} \text{ for }t<-\log p$
CF	$\biggl(\frac{1-p}{1 - p e^{i\,t}}\biggr)^{\!r} \text{ with }t\in\mathbb{R}$
PGF	$\biggl(\frac{1-p}{1 - pz}\biggr)^{\!r} \text{ for }\|z\|<\frac1p$
Fisher information	$\frac{r}{p^2(1-p)}$

In probability theory and statistics, the negative binomial distribution is a discrete probability distribution of the number of successes in a sequence of independent and identically distributed Bernoulli trials before a specified (non-random) number of failures (denoted r) occurs. For example, if we define a "1" as failure, all non-"1"s as successes, and we throw a die repeatedly until the third time “1” appears (r = three failures), then the probability distribution of the number of non-“1”s that had appeared will be a negative binomial.

The Pascal distribution (after Blaise Pascal) and Polya distribution (for George Pólya) are special cases of the negative binomial. There is a convention among engineers, climatologists, and others to reserve “negative binomial” in a strict sense or “Pascal” for the case of an integer-valued stopping-time parameter r, and use “Polya” for the real-valued case.

For occurrences of “contagious” discrete events, like tornado outbreaks, the Polya distributions can be used to give more accurate models than the Poisson distribution by allowing the mean and variance to be different, unlike the Poisson. “Contagious” events have positively correlated occurrences causing a larger variance than if the occurrences were independent, due to a positive covariance term.

Definition

Suppose there is a sequence of independent Bernoulli trials. Thus, each trial has two potential outcomes called “success” and “failure”. In each trial the probability of success is p and of failure is (1 − p). We are observing this sequence until a predefined number r of failures has occurred. Then the random number of successes we have seen, X, will have the negative binomial (or Pascal) distribution:

X\sim\operatorname{NB}(r; p)

When applied to real-world problems, outcomes of success and failure may or may not be outcomes we ordinarily view as good and bad, respectively. Suppose we used the negative binomial distribution to model the number of days a certain machine works before it breaks down. In this case “success” would be the result on a day when the machine worked properly, whereas a breakdown would be a “failure”. If we used the negative binomial distribution to model the number of goal attempts a sportsman makes before scoring r goals, though, then each unsuccessful attempt would be a “success”, and scoring a goal would be “failure”. If we are tossing a coin, then the negative binomial distribution can give the number of heads (“success”) we are likely to encounter before we encounter a certain number of tails (“failure”). In the probability mass function below, p is the probability of success, and (1-p) is the probability of failure.

Probability mass function

The probability mass function of the negative binomial distribution is

f(k; r, p) \equiv \Pr(X = k) = \binom{k+r-1}{k} p^k(1-p)^r \quad\text{for }k = 0, 1, 2, \dotsc

Here the quantity in parentheses is the binomial coefficient, and is equal to

\binom{k+r-1}{k} = \frac{(k+r-1)!}{k!\,(r-1)!} = \frac{(k+r-1)(k+r-2)\dotsm(r)}{k!}.

This quantity can alternatively be written in the following manner, explaining the name “negative binomial”:

\frac{(k+r-1)\dotsm(r)}{k!} = (-1)^k \frac{(-r)(-r-1)(-r-2)\dotsm(-r-k+1)}{k!} = (-1)^k\binom{-r}{k}. \qquad (*)

To understand the above definition of the probability mass function, note that the probability for every specific sequence of k successes and r failures is (1 − p)^rp^k, because the outcomes of the k + r trials are supposed to happen independently. Since the rth failure comes last, it remains to choose the k trials with successes out of the remaining k + r − 1 trials. The above binomial coefficient, due to its combinatorial interpretation, gives precisely the number of all these sequences of length k + r − 1.

The following recurrence relation holds:

\left\{\begin{array}{l} (k+1) \Pr (k+1)-p \Pr (k) (k+r)=0, \\[10pt] \Pr (0)=(1-p)^r \end{array}\right\}

Expectation

The expected total number of trials $k + r$ of a negative binomial distribution with parameters $(r, p)$ is $r /(1- p)$ . To see this intuitively, imagine the above experiment is performed many times. That is, a set of trials is performed until $r$ failures are obtained, then another set of trials, and then another etc. Write down the number of trials performed in each experiment: $a, b, c, \dots$ and set $a + b + c + \dots = N$ . Now we would expect about $N (1- p)$ failures in total. Say the experiment was performed $n$ times. Then there are $nr$ failures in total. So we would expect $nr = N (1- p)$ , so $N / n = r /(1- p)$ . See that $N / n$ is just the average number of trials per experiment. That is what we mean by "expectation". The average number of successes per experiment is $N / n - r$ , which must have expected value equal to $r /(1- p) - r = rp /(1- p)$ . This agrees with the mean given in the box on the right-hand side of this page.

Extension to real-valued r

It is possible to extend the definition of the negative binomial distribution to the case of a positive real parameter r. Although it is impossible to visualize a non-integer number of “failures”, we can still formally define the distribution through its probability mass function.

As before, we say that X has a negative binomial (or Pólya) distribution if it has a probability mass function:

f(k; r, p) \equiv \Pr(X = k) = \binom{k+r-1}{k} p^k (1-p)^r \quad\text{for }k = 0, 1, 2, \dotsc

Here r is a real, positive number. The binomial coefficient is then defined by the multiplicative formula and can also be rewritten using the gamma function:

\binom{k+r-1}{k} = \frac{(k+r-1)(k+r-2)\dotsm(r)}{k!} = \frac{\Gamma(k+r)}{k!\,\Gamma(r)}.

Note that by the binomial series and (*) above, for every 0 ≤ p < 1,

(1-p)^{-r}=\sum_{k=0}^\infty\binom{-r}{k}(-p)^k =\sum_{k=0}^\infty\binom{k+r-1}{k}p^k

hence the terms of the probability mass function indeed add up to one.

Alternative formulations

Some textbooks may define the negative binomial distribution slightly differently from here. The most common variations are:

The definition where X is the total number of trials needed to get r failures, not simply the number of successes. Since the total number of trials is equal to the number of successes plus the number of failures, this definition differs from ours by adding constant r.
In order to convert formulas of this article into this alternative definition, replace everywhere “k” with “k - r”, and also subtract r from the mean, the median, and the mode. In order to convert formulas written with this definition into the one used in the article, replace “k” with “k + r” and add r to the mean, the median and the mode. Effectively, this implies using the probability mass function

$f(k; r, p) \equiv \Pr(X = k \text{ trials}) = \Pr(X = k-r \text{ successes}) = \binom{k-1}{k-r} (1-p)^r p^{k-r} \quad\text{for }k = r, r+1, r+2, \dotsc$

which perhaps resembles the binomial distribution more closely than the version above. Note that the arguments of the binomial coefficient are decremented due to order: the last "failure" must occur last, and so the other events have one fewer positions available when counting possible orderings.
Note that this definition of the negative binomial distribution does not easily generalize to a positive, real parameter r.
The definition where X is the number of “k” failures that occur for a given number of r successes. This gives a distribution for the number of failures instead of the number of successes. ^[2]
In order to convert the original formula of this article into this alternative definition, swap “k” and “r” everywhere, and the binomial coefficient will now be choosing “k - 1” failures (instead of “r” successes) from the first “k + r - 1” trials. Effectively, this implies using the probability mass function

$f(k; r, p) \equiv \Pr(X = k \text{ failures}) = \binom{k+r-1}{k-1} p^r(1-p)^k \quad\text{for }k = 1, 2, 3, \dotsc$
Where “k” is now the number of failures and “r” is now the number of successes. Alternatively, this formulation can also be written in terms of “k” failures and “n” trials by recognizing that n = k + r.
The definition where p denotes the probability of a failure, not of a success. In order to convert formulas between this definition and the one used in the article, replace “p” with “1 − p” everywhere. Likewise, the corresponding text will have the words “failure” and “success” swapped compared with the original case.
The previous condition can be combined simultaneously with an alternative definition of X, i.e. X counts total trials, and p is the probability of failure.
In negative binomial regression,^[3] the distribution is specified in terms of its mean, m, which is then related to explanatory variables as in linear regression or other generalized linear models. The probability mass function then becomes
$\Pr(X = k) = \left(\frac{r}{r+m}\right)^r \frac{\Gamma(r+k)}{k! \, \Gamma(r)} \left(\frac{m}{r+m}\right)^k \quad\text{for }k = 0, 1, 2, \dotsc$ .
The variance can then be written $m+\frac{m^2}{r}$ , and the parameter r is referred to as the “dispersion parameter”, “shape parameter” or “clustering coefficient”,^[4] or the “heterogeneity”^[3] or “aggregation” parameter.^[5] The term “aggregation” is particularly used in ecology when describing counts of individual organisms. Decrease of the aggregation parameter r towards zero corresponds to increasing aggregation of the organisms; increase of r towards infinity corresponds to absence of aggregation, as can be described by Poisson regression. Some implementations of negative binomial regression use the reciprocal of r and refer to that also as the “dispersion parameter”.
Sometimes the distribution is parameterized in terms of its mean μ and variance σ². In that case,
$p=\frac{\sigma^2-\mu}{\sigma^2},$

$r=\frac{\mu^2}{\sigma^2-\mu},$ and

$\Pr(X=k) = {k+\frac{\mu^2}{\sigma^2-\mu}-1 \choose k} \left(\frac{\mu}{\sigma^2}\right)^{\left(\frac{\mu^2}{\sigma^2-\mu}\right)} \left(\frac{\sigma^2-\mu}{\sigma^2}\right)^k.$

Occurrence

Waiting time in a Bernoulli process

For the special case where r is an integer, the negative binomial distribution is known as the Pascal distribution. It is the probability distribution of a certain number of failures and successes in a series of independent and identically distributed Bernoulli trials. For k + r Bernoulli trials with success probability p, the negative binomial gives the probability of k successes and r failures, with a failure on the last trial. In other words, the negative binomial distribution is the probability distribution of the number of successes before the rth failure in a Bernoulli process, with probability p of successes on each trial. A Bernoulli process is a discrete time process, and so the number of trials, failures, and successes are integers.

Consider the following example. Suppose we repeatedly throw a dice, and consider a “1” to be a “failure”. The probability of success on each trial is 5/6. The number of successes before the third failure belongs to the infinite set { 0, 1, 2, 3, ... }. That number of successes is a negative-binomially distributed random variable.

When r = 1 we get the probability distribution of number of successes before the first failure (i.e. the probability of the first failure occurring on the (k + 1)st trial), which is a geometric distribution:

f(k; r, p) = (1-p) \cdot p^k \!

Overdispersed Poisson

The negative binomial distribution, especially in its alternative parameterization described above, can be used as an alternative to the Poisson distribution. It is especially useful for discrete data over an unbounded positive range whose sample variance exceeds the sample mean. In such cases, the observations are overdispersed with respect to a Poisson distribution, for which the mean is equal to the variance. Hence a Poisson distribution is not an appropriate model. Since the negative binomial distribution has one more parameter than the Poisson, the second parameter can be used to adjust the variance independently of the mean. See Cumulants of some discrete probability distributions.

An application of this is to annual counts of tropical cyclones in the North Atlantic or to monthly to 6-monthly counts of wintertime extratropical cyclones over Europe, for which the variance is greater than the mean.^[6]^[7]^[8] In the case of modest overdispersion, this may produce substantially similar results to an overdispersed Poisson distribution.^[9]^[10]

The negative binomial distribution is also commonly used to model gene expression in the form of discrete read count data from high-throughput RNA sequencing experiments.^[11]^[12]

Related distributions

The geometric distribution (on { 0, 1, 2, 3, ... }) is a special case of the negative binomial distribution, with

\text{Geom}(p) = \text{NB}(1,\, 1-p).\,

The negative binomial distribution is a special case of the discrete phase-type distribution.
The negative binomial distribution is a special case of discrete Compound Poisson distribution.

Poisson distribution

Consider a sequence of negative binomial random variables where the stopping parameter r goes to infinity, whereas the probability of success in each trial, p, goes to zero in such a way as to keep the mean of the distribution constant. Denoting this mean λ, the parameter p will be p=λ/(λ+r)

\lambda = r\,\frac{p}{1-p} \quad \Rightarrow \quad p = \frac{\lambda}{r+\lambda}.

Under this parametrization the probability mass function will be

f(k; r, p) = \frac{\Gamma(k+r)}{k!\cdot\Gamma(r)}p^k(1-p)^r = \frac{\lambda^k}{k!} \cdot \frac{\Gamma(r+k)}{\Gamma(r)\;(r+\lambda)^k} \cdot \frac{1}{\left(1+\frac{\lambda}{r}\right)^{r}}

Now if we consider the limit as r → ∞, the second factor will converge to one, and the third to the exponent function:

\lim_{r\to\infty} f(k; r, p) = \frac{\lambda^k}{k!} \cdot 1 \cdot \frac{1}{e^\lambda},

which is the mass function of a Poisson-distributed random variable with expected value λ.

In other words, the alternatively parameterized negative binomial distribution converges to the Poisson distribution and r controls the deviation from the Poisson. This makes the negative binomial distribution suitable as a robust alternative to the Poisson, which approaches the Poisson for large r, but which has larger variance than the Poisson for small r.

\text{Poisson}(\lambda) = \lim_{r \to \infty} \text{NB}\Big(r,\ \frac{\lambda}{\lambda+r}\Big).

Gamma–Poisson mixture

The negative binomial distribution also arises as a continuous mixture of Poisson distributions (i.e. a compound probability distribution) where the mixing distribution of the Poisson rate is a gamma distribution. That is, we can view the negative binomial as a Poisson(λ) distribution, where λ is itself a random variable, distributed as a gamma distribution with shape = r and scale θ = p/(1 − p) or correspondingly rate β = (1 - p)/p.

Formally, this means that the mass function of the negative binomial distribution can be written as

\begin{align} f(k; r, p) & = \int_0^\infty f_{\text{Poisson}(\lambda)}(k) \cdot f_{\text{Gamma}\left(r,\, \frac{1-p}{p}\right)}(\lambda) \; \mathrm{d}\lambda \\[8pt] & = \int_0^\infty \frac{\lambda^k}{k!} e^{-\lambda} \cdot \lambda^{r-1}\frac{e^{-\lambda (1-p)/p}}{\big(\frac{p}{1-p}\big)^r\,\Gamma(r)} \; \mathrm{d}\lambda \\[8pt] & = \frac{(1-p)^r p^{-r}}{k!\,\Gamma(r)} \int_0^\infty \lambda^{r+k-1} e^{-\lambda/p} \;\mathrm{d}\lambda \\[8pt] & = \frac{(1-p)^r p^{-r}}{k!\,\Gamma(r)} \ p^{r+k} \, \Gamma(r+k) \\[8pt] & = \frac{\Gamma(r+k)}{k!\;\Gamma(r)} \; p^k (1-p)^r. \end{align}

Because of this, the negative binomial distribution is also known as the gamma–Poisson (mixture) distribution.

Note: The negative binomial distribution was originally derived as a limiting case of the gamma-Poisson distribution.^[13]

Sum of geometric distributions

If Y_r is a random variable following the negative binomial distribution with parameters r and p, and support {0, 1, 2, ...}, then Y_r is a sum of r independent variables following the geometric distribution (on {0, 1, 2, ...}) with parameter 1-p. As a result of the central limit theorem, Y_r (properly scaled and shifted) is therefore approximately normal for sufficiently large r.

Furthermore, if B_s+r is a random variable following the binomial distribution with parameters s + r and 1 − p, then

\begin{align} \Pr(Y_r \leq s) & {} = 1 - I_p(s+1, r) \\ & {} = 1 - I_{p}((s+r)-(r-1), (r-1)+1) \\ & {} = 1 - \Pr(B_{s+r} \leq r-1) \\ & {} = \Pr(B_{s+r} \geq r) \\ & {} = \Pr(\text{after } s+r \text{ trials, there are at least } r \text{ successes}). \end{align}

In this sense, the negative binomial distribution is the "inverse" of the binomial distribution.

The sum of independent negative-binomially distributed random variables r₁ and r₂ with the same value for parameter p is negative-binomially distributed with the same p but with "r-value" r₁ + r₂.

The negative binomial distribution is infinitely divisible, i.e., if Y has a negative binomial distribution, then for any positive integer n, there exist independent identically distributed random variables Y₁, ..., Y_n whose sum has the same distribution that Y has.

Representation as compound Poisson distribution

The negative binomial distribution NB(r,p) can be represented as a compound Poisson distribution: Let {Y_n, n ∈ ℕ₀} denote a sequence of independent and identically distributed random variables, each one having the logarithmic distribution Log(p), with probability mass function

f(k; r, p) = \frac{-p^k}{k\ln(1-p)},\qquad k\in{\mathbb N}.

Let N be a random variable, independent of the sequence, and suppose that N has a Poisson distribution with mean λ = −r ln(1 − p). Then the random sum

X=\sum_{n=1}^N Y_n

is NB(r,p)-distributed. To prove this, we calculate the probability generating function G_X of X, which is the composition of the probability generating functions G_N and G_Y₁. Using

G_N(z)=\exp(\lambda(z-1)),\qquad z\in\mathbb{R},

and

G_{Y_1}(z)=\frac{\ln(1-pz)}{\ln(1-p)},\qquad |z|<\frac1p,

we obtain

\begin{align}G_X(z) &=G_N(G_{Y_1}(z))\\ &=\exp\biggl(\lambda\biggl(\frac{\ln(1-pz)}{\ln(1-p)}-1\biggr)\biggr)\\ &=\exp\bigl(-r(\ln(1-pz)-\ln(1-p))\bigr)\\ &=\biggl(\frac{1-p}{1-pz}\biggr)^r,\qquad |z|<\frac1p,\end{align}

which is the probability generating function of the NB(r,p) distribution.

The following table describes four distributions related to the number of successes in a sequence of draws:

	With replacements	No replacements
Given number of draws	binomial distribution	hypergeometric distribution
Given number of failures	negative binomial distribution	negative hypergeometric distribution

Properties

Cumulative distribution function

The cumulative distribution function can be expressed in terms of the regularized incomplete beta function:

f(k; r, p) \equiv \Pr(X\le k) = 1 - I_{p}(k+1, r) = I_{1-p}(r,k+1).

Sampling and point estimation of p

Suppose p is unknown and an experiment is conducted where it is decided ahead of time that sampling will continue until r successes are found. A sufficient statistic for the experiment is k, the number of failures.

In estimating p, the minimum variance unbiased estimator is

\hat{p}=\frac{r-1}{r+k-1}.

The maximum likelihood estimate of p is

\tilde{p}=\frac{r}{r+k},

but this is a biased estimate. Its inverse (r + k)/r, is an unbiased estimate of 1/p, however.^[14]

Relation to the binomial theorem

Suppose Y is a random variable with a binomial distribution with parameters n and p. Assume p + q = 1, with p, q >=0. Then the binomial theorem implies that

1=1^n=(p+q)^n=\sum_{k=0}^n {n \choose k} p^k q^{n-k}.

Using Newton's binomial theorem, this can equally be written as:

(p+q)^n=\sum_{k=0}^\infty {n \choose k} p^k q^{n-k},

in which the upper bound of summation is infinite. In this case, the binomial coefficient

{n \choose k}={n(n-1)(n-2)\cdots(n-k+1) \over k! }.

is defined when n is a real number, instead of just a positive integer. But in our case of the binomial distribution it is zero when k > n. We can then say, for example

(p+q)^{8.3}=\sum_{k=0}^\infty {8.3 \choose k} p^k q^{8.3 - k}.

Now suppose r > 0 and we use a negative exponent:

1=p^r\cdot p^{-r}=p^r (1-q)^{-r}=p^r \sum_{k=0}^\infty {-r \choose k} (-q)^k.

Then all of the terms are positive, and the term

p^r {-r \choose k} (-q)^k

is just the probability that the number of failures before the rth success is equal to k, provided r is an integer. (If r is a negative non-integer, so that the exponent is a positive non-integer, then some of the terms in the sum above are negative, so we do not have a probability distribution on the set of all nonnegative integers.)

Now we also allow non-integer values of r. Then we have a proper negative binomial distribution, which is a generalization of the Pascal distribution, which coincides with the Pascal distribution when r happens to be a positive integer.

Recall from above that

This property persists when the definition is thus generalized, and affords a quick way to see that the negative binomial distribution is infinitely divisible.

Parameter estimation

Maximum likelihood estimation

The maximum likelihood estimator only exists for samples for which the sample variance is larger than the sample mean.^[15] The likelihood function for N iid observations (k₁, ..., k_N) is

L(r,p)=\prod_{i=1}^N f(k_i;r,p)\,\!

from which we calculate the log-likelihood function

\ell(r,p) = \sum_{i=1}^N \ln{(\Gamma(k_i + r))} - \sum_{i=1}^N \ln(k_i !) - N\ln{(\Gamma(r))} + \sum_{i=1}^N k_i \ln{(p)} + Nr \ln(1-p).

To find the maximum we take the partial derivatives with respect to r and p and set them equal to zero:

\frac{\partial \ell(r,p)}{\partial p} = \left[\sum_{i=1}^N k_i \frac{1}{p}\right] - Nr \frac{1}{1-p} = 0

and

\frac{\partial \ell(r,p)}{\partial r} = \left[\sum_{i=1}^N \psi(k_i + r)\right] - N\psi(r) + N\ln{(1-p)} = 0

where

\psi(k) = \frac{\Gamma'(k)}{\Gamma(k)} \!

is the digamma function.

Solving the first equation for p gives:

p = \frac{\sum_{i=1}^N k_i} {Nr + \sum_{i=1}^N k_i}

Substituting this in the second equation gives:

\frac{\partial \ell(r,p)}{\partial r} = \left[\sum_{i=1}^N \psi(k_i + r)\right] - N\psi(r) + N\ln{\left(\frac{r}{r + \sum_{i=1}^N k_i/N}\right)} = 0

This equation cannot be solved for r in closed form. If a numerical solution is desired, an iterative technique such as Newton's method can be used. Alternatively, the expectation–maximization algorithm can be used.^[15]

Examples

Selling candy

Pat is required to sell candy bars to raise money for the 6th grade field trip. There are thirty houses in the neighborhood, and Pat is not supposed to return home until five candy bars have been sold. So the child goes door to door, selling candy bars. At each house, there is a 0.4 probability of selling one candy bar and a 0.6 probability of selling nothing.

What's the probability of selling the last candy bar at the nth house?

Recall that the NegBin(r, p) distribution describes the probability of k failures and r successes in k + r Bernoulli(p) trials with success on the last trial. Selling five candy bars means getting five successes. The number of trials (i.e. houses) this takes is therefore k + 5 = n. The random variable we are interested in is the number of houses, so we substitute k = n − 5 into a NegBin(5, 0.4) mass function and obtain the following mass function of the distribution of houses (for n ≥ 5):

f(n) = {(n-5) + 5 - 1 \choose n-5} \; 0.4^5 \; 0.6^{n-5} = {n-1 \choose n-5} \; 2^5 \; \frac{3^{n-5}}{5^n}.

What's the probability that Pat finishes on the tenth house?

f(10) = 0.1003290624. \,

What's the probability that Pat finishes on or before reaching the eighth house?

To finish on or before the eighth house, Pat must finish at the fifth, sixth, seventh, or eighth house. Sum those probabilities:

f(5) = 0.01024 \,

f(6) = 0.03072 \,

f(7) = 0.055296 \,

f(8) = 0.0774144 \,

\sum_{j=5}^8 f(j) = 0.17367.

What's the probability that Pat exhausts all 30 houses in the neighborhood?

This can be expressed as the probability that Pat does not finish on the fifth through the thirtieth house:

1-\sum_{j=5}^{30} f(j) = 1 - I_{0.4}(5, 30-5+1) \approx 1 - 0.99849 = 0.00151.

Length of hospital stay

Hospital length of stay is an example of real world data that can be modelled well with a negative binomial distribution.^[16]

References

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↑ Greenwood, M.; Yule, G. U. (1920). "An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference of multiple attacks of disease or of repeated accidents". J R Statist Soc 83 (2): 255–279. doi:10.2307/2341080.
↑ Haldane, J. B. S. (1945). "On a Method of Estimating Frequencies". Biometrika 33 (3): 222–225. JSTOR 2332299.
1 2 Aramidis, K. (1999). "An EM algorithm for estimating negative binomial parameters". Australian & New Zealand Journal of Statistics 41 (2): 213–221. doi:10.1111/1467-842X.00075.
↑ Carter, E.M., Potts, H.W.W. (4 April 2014). "Predicting length of stay from an electronic patient record system: a primary total knee replacement example". BMC Medical Informatics & Decision Making 14: 26. doi:10.1186/1472-6947-14-26. Retrieved 23 April 2014.

Probability distributions

List

Discrete univariate with finite support	Benford Bernoulli beta-binomial binomial categorical hypergeometric Poisson binomial Rademacher discrete uniform Zipf Zipf–Mandelbrot

Discrete univariate with infinite support	beta negative binomial Borel Conway–Maxwell–Poisson discrete phase-type Delaporte extended negative binomial Gauss–Kuzmin geometric logarithmic negative binomial parabolic fractal Poisson Skellam Yule–Simon zeta

Continuous univariate supported on a bounded interval	arcsine ARGUS Balding–Nichols Bates beta beta rectangular Irwin–Hall Kumaraswamy logit-normal noncentral beta raised cosine reciprocal triangular U-quadratic uniform Wigner semicircle

Continuous univariate supported on a semi-infinite interval	Benini Benktander 1st kind Benktander 2nd kind beta prime Burr chi-squared chi Dagum Davis exponential-logarithmic Erlang exponential F folded normal Flory–Schulz Fréchet gamma gamma/Gompertz generalized inverse Gaussian Gompertz half-logistic half-normal Hotelling's T-squared hyper-Erlang hyperexponential hypoexponential inverse chi-squared scaled inverse chi-squared inverse Gaussian inverse gamma Kolmogorov Lévy log-Cauchy log-Laplace log-logistic log-normal matrix-exponential Maxwell–Boltzmann Maxwell–Jüttner Mittag-Leffler Nakagami noncentral chi-squared Pareto phase-type poly-Weibull Rayleigh relativistic Breit–Wigner Rice shifted Gompertz truncated normal type-2 Gumbel Weibull Discrete Weibull Wilks's lambda

Continuous univariate supported on the whole real line	Cauchy exponential power Fisher's z Gaussian q generalized normal generalized hyperbolic geometric stable Gumbel Holtsmark hyperbolic secant Johnson's S_U Landau Laplace asymmetric Laplace logistic noncentral t normal (Gaussian) normal-inverse Gaussian skew normal slash stable Student's t type-1 Gumbel Tracy–Widom variance-gamma Voigt

Continuous univariate with support whose type varies	generalized extreme value generalized Pareto Tukey lambda q-Gaussian q-exponential q-Weibull shifted log-logistic

Mixed continuous-discrete univariate	rectified Gaussian

Multivariate (joint)	Discrete Ewens multinomial Dirichlet-multinomial negative multinomial Continuous Dirichlet generalized Dirichlet multivariate normal multivariate stable multivariate t normal-inverse-gamma normal-gamma Matrix-valued inverse matrix gamma inverse-Wishart matrix normal matrix t matrix gamma normal-inverse-Wishart normal-Wishart Wishart

Directional	Univariate (circular) directional Circular uniform univariate von Mises wrapped normal wrapped Cauchy wrapped exponential wrapped asymmetric Laplace wrapped Lévy Bivariate (spherical) Kent Bivariate (toroidal) bivariate von Mises Multivariate von Mises–Fisher Bingham

Degenerate and singular	Degenerate Dirac delta function Singular Cantor

Families	Circular compound Poisson elliptical exponential natural exponential location-scale maximum entropy mixture Pearson Tweedie wrapped

Some common univariate probability distributions

Continuous	beta Cauchy chi-squared exponential F gamma Laplace log-normal normal Pareto Student's t uniform Weibull

Discrete	Bernoulli binomial discrete uniform geometric hypergeometric negative binomial Poisson

List of probability distributions

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