# sympy.stats.Wald() in Python

• Last Updated : 08 Jun, 2020

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, lamda)`
Where, mean and lamda 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``)``lamda ``=` `Symbol(``"lamda"``, positive ``=` `True``)`` ` `# Using sympy.stats.Wald() method``X ``=` `Wald(``"x"``, mean, lamda)``gfg ``=` `density(X)(z)`` ` `pprint(gfg)`

Output :

2
-lamda*(-mean + z)
——————–
____ 2
___ _______ / 1 2*mean *z
\/ 2 *\/ lamda * / — *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``lamda ``=` `2.35`` ` `# Using sympy.stats.Wald() method``X ``=` `Wald(``"x"``, mean, lamda)``gfg ``=` `density(X)(z)`` ` `pprint(gfg)`

Output :

0.498668646362573
—————–
____
\/ pi

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