  # scipy stats.burr () | python

NumPy | Python Methods and Functions

Parameters:
q: lower and upper tail probability
a, b: shape parameters
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: burr continuous random variable

Code # 1: Generating a continuous random variable

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` # scipy import from scipy.stats import burr   numargs = burr.numargs [a, b] = [ 0.6 ,] * numargs rv = burr (a, b)    print ( "RV:" , rv) `

Output:

` RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029482FCC438 & gt; `

Code # 2: beta random variations and probability distribution function.

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 ` import ` ` numpy as np ` ` quantile ` ` = ` ` np.arange (` ` 0.01 ` `, ` ` 1 ` `, ` ` 0.1 ` `) `   ` # Random Variants ` ` R ` ` = ` ` burr.rvs (a, b, scale ` ` = ` ` 2 ` `, size ` ` = ` ` 10 ` `) ` ` p rint ` ` (` ` "Random Variates:" ` `, R) `   ` # PDF ` ` R ` ` = ` ` burr.pdf (quantile, a, b, loc ` ` = ` ` 0 ` `, scale ` ` = ` ` 1 ` `) ` ` print ` ` (` ` "Probability Distribution:" ` `, R) `

Output:

` Random Variates: [1.51241629e-04 3.47964171e-01 2.94154949e-02 5.10430246e-02 1.82413279e-02 2.12564883e + 00 3.51099766e-05 2.32907895e + 01 6.24723647e-04 2.79124934e-01] Probability Distribution: [6.21994723 1.01375434 0.57575653 0.40021455 0.30462819 0.24439598 0.20298921 0.17281591 0.14988693 0.1319016] `

Code # 3: Graphic representation.

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 ` import numpy as np `` import matplotlib.pyplot as plt   distribution = np.linspace ( 0 , np.minimum (rv. dist.b, 5 )) print ( "Distribution:" , distribution)   plot = plt.plot (distribution, rv.pdf (distribution)) `

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Output:

` Distribution: [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ] `

Code # 4: Various Positional Arguments

 ` import ` ` matplotlib. pyplot as plt ` ` import ` ` numpy as np `   ` x ` ` = ` ` np.linspace (` ` 0 ` `, ` ` 1.0 ` `, ` ` 100 ` `) `   ` # Various positional arguments ` ` y1 ` ` = ` ` burr.pdf (x, 2.75 , 2.75 ) `` y2 = burr.pdf (x, 3.25 , 3.25 ) plt.plot (x, y1, " * " , x, y2, " r-- " ) `

Output: