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