negative binomial distribution in a sentence

Examples

1. The family of negative binomial distributions with fixed number of failures ( a . k . a . stopping-time parameter ) " r " is an exponential family.
2. Because an estimate of the variance of " IP " is extremely difficult to estimate from the formula itself, LLyod suggested fitting a negative binomial distribution to the data.
3. The gamma distribution is also used to model errors in multi-level Poisson regression models, because the combination of the Poisson distribution and a gamma distribution is a negative binomial distribution.
4. Initial attempts to explain the spatial distribution of animals had been based on approaches like Bartlett s stochastic population models and the negative binomial distribution that could result from birth-death processes.
5. In contrast, VGLMs offer a much richer set of models to handle overdispersion with respect to the Poisson, e . g ., the negative binomial distribution and several variants thereof.
6. For example, given a Bayes network with a set of conditionally independent identically distributed Poisson-distributed nodes causes the conditional distribution of one node given the others to assume a negative binomial distribution.
7. As these may be biased by small samples an alternative is the " U " statistic-the difference between the variance expected under the negative binomial distribution and that of the sample.
8. 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.
9. Bernoulli trials may also lead to negative binomial distributions ( which count the number of successes in a series of repeated Bernoulli trials until a specified number of failures are seen ), as well as various other distributions.
10. The Poisson distribution, the negative binomial distribution, the Gamma distribution and the degenerate distribution are examples of infinitely divisible distributions; as are the normal distribution, Cauchy distribution and all other members of the stable distribution family.