Scipy stats.halfgennorm() | Python

Scipy stats.halfgennorm() | Python

The halfgennorm() function is available in the scipy.stats module and represents the half generalized normal distribution. The half generalized normal distribution is useful in modeling non-negative data with a sharp peak at zero.

The halfgennorm distribution has one shape parameter, ��, which represents the shape of the distribution. When ��=1, it becomes the standard half-normal distribution.

Here's a brief overview of the halfgennorm() function and how you can use it:

  1. Probability Density Function (pdf) and Cumulative Density Function (cdf):

    from scipy.stats import halfgennorm import numpy as np import matplotlib.pyplot as plt beta = 0.675 x = np.linspace(0, 5, 1000) # PDF pdf_values = halfgennorm.pdf(x, beta) plt.plot(x, pdf_values, label='PDF') # CDF cdf_values = halfgennorm.cdf(x, beta) plt.plot(x, cdf_values, label='CDF') plt.legend() plt.show()

  2. Random Variate Generation:

    r = halfgennorm.rvs(beta, size=1000) plt.hist(r, bins=30, density=True) plt.plot(x, pdf_values) plt.show()

  3. Statistical Measures:

    mean, var, skew, kurt = halfgennorm.stats(beta, moments='mvsk') print("Mean:", mean) print("Variance:", var) print("Skewness:", skew) print("Kurtosis:", kurt)

The above examples illustrate how to use the halfgennorm() function to generate the PDF, CDF, random variates, and also compute statistical measures of the distribution for a given �� value.

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