sympy.stats.Wald() in Python
Last Updated :
05 Aug, 2022
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.
from sympy.stats import Wald, density
from sympy import Symbol, pprint
z = Symbol( "z" )
mean = Symbol( "mean" , positive = True )
lambda = Symbol( "lambda" , positive = True )
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 :
from sympy.stats import Wald, density
from sympy import Symbol, pprint
z = 0.86
mean = 6
lambda = 2.35
X = Wald( "x" , mean, lambda )
gfg = density(X)(z)
pprint(gfg)
|
Output :
0.498668646362573
—————–
____
\/ pi
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