Python - Moyal Distribution in Statistics Last Updated : 31 Dec, 2019 Comments Improve Suggest changes Like Article Like Report scipy.stats.moyal() is a Moyal continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution. 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. moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’). Results : Moyal continuous random variable Code #1 : Creating Moyal continuous random variable Python3 1== # importing library from scipy.stats import moyal numargs = moyal.numargs a, b = 4.32, 3.18 rv = moyal(a, b) print ("RV : \n", rv) Output : RV : scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D65832480x000002A9D6583248 Code #2 : Moyal continuous variates and probability distribution Python3 1== import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = moyal.rvs(a, b) print ("Random Variates : \n", R) # PDF R = moyal.pdf(a, b, quantile) print ("\nProbability Distribution : \n", R) Output : Random Variates : -0.10758844036768522 Probability Distribution : [7.01656572e-24 2.03739617e-02 1.25585161e-01 2.02076426e-01 2.34901631e-01 2.42507808e-01 2.37825337e-01 2.27718227e-01 2.15604463e-01 2.03139800e-01] Code #3 : Graphical Representation. Python3 1== import numpy as np import matplotlib.pyplot as plt distribution = np.linspace(0, np.minimum(rv.dist.b, 3)) print("Distribution : \n", distribution) plot = plt.plot(distribution, rv.pdf(distribution)) Output : Distribution : [0. 0.04081633 0.08163265 0.12244898 0.16326531 0.20408163 0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959 0.48979592 0.53061224 0.57142857 0.6122449 0.65306122 0.69387755 0.73469388 0.7755102 0.81632653 0.85714286 0.89795918 0.93877551 0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347 1.2244898 1.26530612 1.30612245 1.34693878 1.3877551 1.42857143 1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939 1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735 1.95918367 2. ] Code #4 : Varying Positional Arguments Python3 1== import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 5, 100) # Varying positional arguments y1 = moyal .pdf(x, 1, 3) y2 = moyal .pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") Output : Comment More infoAdvertise with us Next Article Python - Lomax Distribution in Statistics M mathemagic Follow Improve Article Tags : Python Python scipy-stats-functions Practice Tags : python Similar Reads Python - Maxwell Distribution in Statistics scipy.stats.maxwell() is a Maxwell (Pareto of the second kind) continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution. Parameters : q : lower and upper tail pro 2 min read Python - Maxwell Distribution in Statistics scipy.stats.maxwell() is a Maxwell (Pareto of the second kind) continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution. Parameters : q : lower and upper tail pro 2 min read Python - Lomax Distribution in Statistics scipy.stats.lomax() is a Lomax (Pareto of the second kind) continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution. Parameters : q : lower and upper tail probabi 2 min read Python - Lomax Distribution in Statistics scipy.stats.lomax() is a Lomax (Pareto of the second kind) continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution. Parameters : q : lower and upper tail probabi 2 min read Python - Mielke Distribution in Statistics scipy.stats.mielke() is a Mielke Beta-Kappa / Dagum continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution. Parameters : q : lower and upper tail probability x 2 min read Python - Levy Distribution in Statistics scipy.stats.levy() is a levy continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution. Parameters : q : lower and upper tail probability x : quantiles loc : [opti 2 min read Like