Parameters:
-" q: lower and upper tail probability
-" x: quantiles
-" loc: [optional] location parameter. Default = 0
-" scale: [optional] scale parameter. Default = 1
-" size: [tuple of ints, optional] shape or random variates.
-" a, b, c, z: shape parameters
-" moments: [optional] composed of letters [’mvsk’]; ’m’ = mean, ’v’ = variance,
’s’ = Fisher’s skew and ’k’ = Fisher’s kurtosis. (default = ’mv’).Results: Gauss hyper-geometric continuous random variable
Code # 1: Create hypergeometric continuous Gaussian random variable
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Output:
RV: "scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E399AB5A58"
Code # 2: Gaussian Hypergeometric Random Variables and Probability Distribution.
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Output:
Random Variates: [1.45915082 0.58184603 1.91448022 1.23505789 0.9253147 0.36681062 0.19628827 0.91795248 1.95313724 1.63728124] Probability Distribution: [0.83983413 0.82838709 0.81749232 0.80714179 0.79731436 0.78798255 0.77911641 0.77068563 0.76266077 0.75501387]
Code # 3: Graphic representation.
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Output:
Distribution: [0. 0.02040816 0.04081633 0.06122449 0.08163265 0.10204082 0.12244898 0.14285714 0.16326531 0.18367347 0.20408163 0.2244898 0.24489796 0.26530612 0.28571429 0.30612245 0.32653061 0.34693878 0.36734694 0.3877551 0.40816327 0.42857143 0.44897959 0.46938776 0.48979592 0.51020408 0.53061224 0.55102041 0.57142857 0.59183673 0.6122449 0.63265306 0.65306122 0.67346939 0.69387755 0.71428571 0.73469388 0.75510204 0.7755102 0.79591837 0.81632653 0.83673469 0.85714286 0.87755102 0.89795918 0.91836735 0.93877551 0.95918367 0.97959184 1. ]
Code # 4: Various Positional Arguments
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Exit:
