dask.array.random.negative_binomial
dask.array.random.negative_binomial¶
- dask.array.random.negative_binomial(*args, **kwargs)¶
Draw samples from a negative binomial distribution.
This docstring was copied from numpy.random.mtrand.RandomState.negative_binomial.
Some inconsistencies with the Dask version may exist.
Samples are drawn from a negative binomial distribution with specified parameters, n successes and p probability of success where n is > 0 and p is in the interval [0, 1].
Note
New code should use the ~numpy.random.Generator.negative_binomial method of a ~numpy.random.Generator instance instead; please see the Quick start.
- Parameters
- nfloat or array_like of floats
Parameter of the distribution, > 0.
- pfloat or array_like of floats
Parameter of the distribution, >= 0 and <=1.
- sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifn
andp
are both scalars. Otherwise,np.broadcast(n, p).size
samples are drawn.
- Returns
- outndarray or scalar
Drawn samples from the parameterized negative binomial distribution, where each sample is equal to N, the number of failures that occurred before a total of n successes was reached.
Warning
This function returns the C-long dtype, which is 32bit on windows and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones). Since NumPy 2.0, NumPy’s default integer is 32bit on 32bit platforms and 64bit on 64bit platforms.
See also
random.Generator.negative_binomial
which should be used for new code.
Notes
The probability mass function of the negative binomial distribution is
\[P(N;n,p) = \frac{\Gamma(N+n)}{N!\Gamma(n)}p^{n}(1-p)^{N},\]where \(n\) is the number of successes, \(p\) is the probability of success, \(N+n\) is the number of trials, and \(\Gamma\) is the gamma function. When \(n\) is an integer, \(\frac{\Gamma(N+n)}{N!\Gamma(n)} = \binom{N+n-1}{N}\), which is the more common form of this term in the pmf. The negative binomial distribution gives the probability of N failures given n successes, with a success on the last 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.
References
- 1
Weisstein, Eric W. “Negative Binomial Distribution.” From MathWorld–A Wolfram Web Resource. https://mathworld.wolfram.com/NegativeBinomialDistribution.html
- 2
Wikipedia, “Negative binomial distribution”, https://en.wikipedia.org/wiki/Negative_binomial_distribution
Examples
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.?
>>> s = np.random.negative_binomial(1, 0.1, 100000) >>> for i in range(1, 11): ... probability = sum(s<i) / 100000. ... print(i, "wells drilled, probability of one success =", probability)