mars.tensor.random.negative_binomial(n, p, size=None, chunk_size=None, gpu=None, dtype=None)[source]#

Draw samples from a negative binomial distribution.

Samples are drawn from a negative binomial distribution with specified parameters, n trials and p probability of success where n is an integer > 0 and p is in the interval [0, 1].

  • n (int or array_like of ints) – Parameter of the distribution, > 0. Floats are also accepted, but they will be truncated to integers.

  • p (float or array_like of floats) – Parameter of the distribution, >= 0 and <=1.

  • size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if n and p are both scalars. Otherwise, np.broadcast(n, p).size samples are drawn.

  • chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension

  • gpu (bool, optional) – Allocate the tensor on GPU if True, False as default

  • dtype (data-type, optional) – Data-type of the returned tensor.


out – Drawn samples from the parameterized negative binomial distribution, where each sample is equal to N, the number of trials it took to achieve n - 1 successes, N - (n - 1) failures, and a success on the, (N + n)th trial.

Return type

Tensor or scalar


The probability density for the negative binomial distribution is

\[P(N;n,p) = \binom{N+n-1}{n-1}p^{n}(1-p)^{N},\]

where \(n-1\) is the number of successes, \(p\) is the probability of success, and \(N+n-1\) is the number of trials. The negative binomial distribution gives the probability of n-1 successes and N failures in N+n-1 trials, and success on the (N+n)th trial.

If one throws a die repeatedly until the third time a “1” appears, then the probability distribution of the number of non-“1”s that appear before the third “1” is a negative binomial distribution.



Weisstein, Eric W. “Negative Binomial Distribution.” From MathWorld–A Wolfram Web Resource.


Wikipedia, “Negative binomial distribution”,


Draw samples from the distribution:

A real world example. A company drills wild-cat oil exploration wells, each with an estimated probability of success of 0.1. What is the probability of having one success for each successive well, that is what is the probability of a single success after drilling 5 wells, after 6 wells, etc.?

>>> import mars.tensor as mt
>>> s = mt.random.negative_binomial(1, 0.1, 100000)
>>> for i in range(1, 11):
...    probability = (mt.sum(s<i) / 100000.).execute()
...    print i, "wells drilled, probability of one success =", probability