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)
——————–
____ 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 = 0.86mean = 6lambda = 2.35 # Using sympy.stats.Wald() method X = Wald("x", mean, lambda) gfg = density(X)(z) pprint(gfg) |
Output :
0.498668646362573
—————–
____
\/ pi