mars.tensor.random.
chisquare
Draw samples from a chi-square distribution.
When df independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This distribution is often used in hypothesis testing.
df (float or array_like of floats) – Number of degrees of freedom, should be > 0.
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 df is a scalar. Otherwise, mt.array(df).size samples are drawn.
(m, n, k)
m * n * k
None
df
mt.array(df).size
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 chi-square distribution.
Tensor or scalar
ValueError – When df <= 0 or when an inappropriate size (e.g. size=-1) is given.
size=-1
Notes
The variable obtained by summing the squares of df independent, standard normally distributed random variables:
is chi-square distributed, denoted
The probability density function of the chi-squared distribution is
where \(\Gamma\) is the gamma function,
References
NIST “Engineering Statistics Handbook” http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm
Examples
>>> import mars.tensor as mt
>>> mt.random.chisquare(2,4).execute() array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272])