sympy.stats.Wald() in Python
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)
βββββββ
____ 2
___ _______ / 1 2*mean *z
\/ 2 *\/ lambda * / β *e
/ 3
\/ z
ββββββββββββββββ
____
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
ββββββ
____
\/ pi
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