mars.tensor.random.poisson(lam=1.0, size=None, chunk_size=None, gpu=None, dtype=None)[source]#

Draw samples from a Poisson distribution.

The Poisson distribution is the limit of the binomial distribution for large N.

  • lam (float or array_like of floats) – Expectation of interval, should be >= 0. A sequence of expectation intervals must be broadcastable over the requested size.

  • 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 lam is a scalar. Otherwise, mt.array(lam).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 Poisson distribution.

Return type

Tensor or scalar


The Poisson distribution

\[f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!}\]

For events with an expected separation \(\lambda\) the Poisson distribution \(f(k; \lambda)\) describes the probability of \(k\) events occurring within the observed interval \(\lambda\).

Because the output is limited to the range of the C long type, a ValueError is raised when lam is within 10 sigma of the maximum representable value.



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


Wikipedia, “Poisson distribution”,


Draw samples from the distribution:

>>> import mars.tensor as mt
>>> s = mt.random.poisson(5, 10000)

Display histogram of the sample:

>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s.execute(), 14, normed=True)

Draw each 100 values for lambda 100 and 500:

>>> s = mt.random.poisson(lam=(100., 500.), size=(100, 2))