Hướng dẫn generalized chi-square distribution python
A chi-squared continuous random variable. For the noncentral chi-square distribution, see
As an instance of the Notes The probability density function for
\[f(x, k) = \frac{1}{2^{k/2} \Gamma \left( k/2 \right)} x^{k/2-1} \exp \left( -x/2 \right)\] for \(x > 0\) and \(k > 0\) (degrees of freedom, denoted
The chi-squared distribution is a special case of the gamma distribution, with gamma parameters The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the Examples >>> from scipy.stats import chi2 >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(1, 1) Calculate the first four moments: >>> df = 55 >>> mean, var, skew, kurt = chi2.stats(df, moments='mvsk') Display the probability density function ( >>> x = np.linspace(chi2.ppf(0.01, df), ... chi2.ppf(0.99, df), 100) >>> ax.plot(x, chi2.pdf(x, df), ... 'r-', lw=5, alpha=0.6, label='chi2 pdf') Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed. Freeze the distribution and display the frozen >>> rv = chi2(df) >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') Check accuracy of >>> vals = chi2.ppf([0.001, 0.5, 0.999], df) >>> np.allclose([0.001, 0.5, 0.999], chi2.cdf(vals, df)) True Generate random numbers: >>> r = chi2.rvs(df, size=1000) And compare the histogram: >>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) >>> ax.legend(loc='best', frameon=False) >>> plt.show() Methods
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