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Different texts (and even different parts of this article) 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 identifying the specific parametrization used is crucial 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 | |||
---|---|---|---|
Parameters |
r > 0 — number of successes 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 failures | ||
PMF | involving a binomial coefficient | ||
CDF | the regularized incomplete beta function | ||
Mean | |||
Mode | |||
Variance | |||
Skewness | |||
Excess kurtosis | |||
MGF | |||
CF | |||
PGF | |||
Fisher information | |||
Method of moments |
|
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.
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 | |||
---|---|---|---|
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 | involving a binomial coefficient | ||
CDF | the regularized incomplete beta function | ||
Mean | |||
Mode | |||
Variance | |||
Skewness | |||
Excess kurtosis | |||
MGF | |||
CF | |||
PGF | |||
Fisher information |
Probability density function | |||
Cumulative distribution function | |||
Parameters |
| ||
---|---|---|---|
Support | |||
CDF | |||
Mean | |||
Median | No simple closed form | No simple closed form | |
Mode | |||
Variance | |||
Skewness | |||
Excess kurtosis | |||
Entropy | |||
MGF | |||
CF |
- ^ a b DeGroot, Morris H. (1986). Probability and Statistics (Second ed.). Addison-Wesley. pp. 258–259. ISBN 0-201-11366-X. LCCN 84006269. OCLC 10605205. Cite error: The named reference "DeGrootNB" was defined multiple times with different content (see the help page).