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sympy.stats.Wald() in Python

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  • Last Updated : 05 Aug, 2022
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With the help of sympy.stats.Wald() method, we can get the continuous random variable which represents the inverse gaussian distribution as well as Wald distribution by using this method.

Syntax : sympy.stats.Wald(name, mean, lambda)
Where, mean and lambda are positive number.

Return : Return the continuous random variable.

Example #1 :
In this example we can see that by using sympy.stats.Wald() method, we are able to get the continuous random variable representing inverse gaussian or wald distribution by using this method.




# Import sympy and Wald
from sympy.stats import Wald, density
from sympy import Symbol, pprint
  
z = Symbol("z")
mean = Symbol("mean", positive = True)
lambda = Symbol("lambda", positive = True)
  
# Using sympy.stats.Wald() method
X = Wald("x", mean, lambda)
gfg = density(X)(z)
  
pprint(gfg)


Output :

2
-lambda*(-mean + z)
——————–
____ 2
___ _______ / 1 2*mean *z
\/ 2 *\/ lambda * / — *e
/ 3
\/ z
———————————————–
____
2*\/ pi

Example #2 :




# Import sympy and Wald
from sympy.stats import Wald, density
from sympy import Symbol, pprint
  
z = 0.86
mean = 6
lambda = 2.35
  
# Using sympy.stats.Wald() method
X = Wald("x", mean, lambda)
gfg = density(X)(z)
  
pprint(gfg)


Output :

0.498668646362573
—————–
____
\/ pi


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