Chi-square distribution¶

mpsci.distributions.chi2.entropy(k)¶: Differential entropy of the chi-square distribution.

mpsci.distributions.chi2.invcdf(p, k, solver='bisect', **kwargs)¶

Inverse of the CDF for the chi-square distribution.

Also known as the quantile function.

Additional keyword arguments are passed on to mp.findroot().

If not given in kwargs, this function overrides the default maxsteps of mp.findroot and uses (in effect) 4*mp.prec.

Experimental!

mpsci.distributions.chi2.invcdf_k(x, p, solver='bisect', **kwargs)¶

Inverse with respect to k of the CDF chi2.cdf(x, k).

Additional keyword arguments are passed on to mp.findroot().

If not given in kwargs, this function overrides the default maxsteps of mp.findroot and uses (in effect) 4*mp.prec.

Experimental!

mpsci.distributions.chi2.invsf(p, k, solver='bisect', **kwargs)¶

Inverse of the survival function for the chi-square distribution.

Also known as the complemented quantile function.

Additional keyword arguments are passed on to mp.findroot().

If not given in kwargs, this function overrides the default maxsteps of mp.findroot and uses (in effect) 4*mp.prec.

Experimental!

mpsci.distributions.chi2.invsf_k(x, p, solver='bisect', **kwargs)¶

Inverse with respect to k of chi2.sf(x, k).

Additional keyword arguments are passed on to mp.findroot().

If not given in kwargs, this function overrides the default maxsteps of mp.findroot and uses (in effect) 4*mp.prec.

Experimental!

mpsci.distributions.chi2.logpdf(x, k)¶: Logarithm of the PDF for the chi-square distribution.

mpsci.distributions.chi2.mode(k)¶

Mode of the chi-square distribution.

The mode is max(k - 2, 0).

mpsci.distributions.chi2.noncentral_moment(n, k)¶

n-th noncentral moment of the chi-square distribution.

n must be a nonnegative integer.

mpsci.distributions.chi2.sf(x, k)¶: Survival function for the chi-square distribution.

mpsci.distributions.chi2.support(k)¶: Support for the chi-square distribution.