Python – Reciprocal Inverse Gaussian Distribution in Statistics

scipy.stats.recipinvgauss() is a reciprocal inverse Gaussian 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 [â€˜mvskâ€™]; â€˜mâ€™ = mean, â€˜vâ€™ = variance, â€˜sâ€™ = Fisherâ€™s skew and â€˜kâ€™ = Fisherâ€™s kurtosis. (default = â€˜mvâ€™). Results : reciprocal inverse Gaussian continuous random variable
Code #1 : Creating reciprocal inverse Gaussian continuous random variable
 `# importing library `` ` `from` `scipy.stats ``import` `recipinvgauss    ``   ` `numargs ``=` `recipinvgauss   .numargs  ``a, b ``=` `4.32``, ``3.18``rv ``=` `recipinvgauss   (a, b)  ``   ` `print` `(``"RV : \n"``, rv)  `

Output :
```RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D843A9C8
```
Code #2 : reciprocal inverse Gaussian continuous variates and probability distribution
 `import` `numpy as np  ``quantile ``=` `np.arange (``0.01``, ``1``, ``0.1``)  `` ` `# Random Variates  ``R ``=` `recipinvgauss  .rvs(a, b)  ``print` `(``"Random Variates : \n"``, R)  `` ` `# PDF  ``R ``=` `recipinvgauss  .pdf(a, b, quantile)  ``print` `(``"\nProbability Distribution : \n"``, R)   `

Output :
```Random Variates :
6.540700180076524

Probability Distribution :
[0.03015471 0.03206632 0.03410829 0.03629051 0.03862377 0.04111981
0.04379146 0.04665275 0.04971902 0.05300712]

```
Code #3 : Graphical Representation.
 `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
 `import` `matplotlib.pyplot as plt  ``import` `numpy as np  ``    ` `x ``=` `np.linspace(``0``, ``5``, ``100``)  ``    ` `# Varying positional arguments  ``y1 ``=` `recipinvgauss   .pdf(x, ``1``, ``3``, ``5``)  ``y2 ``=` `recipinvgauss   .pdf(x, ``1``, ``4``, ``4``)  ``plt.plot(x, y1, ``"*"``, x, y2, ``"r--"``)  `

Output :

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