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sympy.stats.Moyal() in python
• Last Updated : 05 Jun, 2020

With the help of `sympy.stats.Moyal()` method, we can get the continuous random variable which represents the moyal distribution.

Syntax : `sympy.stats.Moyal(name, mu, sigma)`
Where, mu and sigma are real number.
Return : Return the continuous random variable.

Example #1 :
In this example we can see that by using `sympy.stats.Moyal()` method, we are able to get the continuous random variable representing moyal distribution by using this method.

 `# Import sympy and Moyal``from` `sympy.stats ``import` `Moyal, density``from` `sympy ``import` `Symbol, pprint`` ` `z ``=` `Symbol(``"z"``)``mu ``=` `Symbol(``"mu"``, positive ``=` `True``)``sigma ``=` `Symbol(``"sigma"``, positive ``=` `True``)`` ` `# Using sympy.stats.Moyal() method``X ``=` `Moyal(``"x"``, mu, sigma)``gfg ``=` `density(X)(z)`` ` `print``(gfg)`

Output :

sqrt(2)*exp(-exp((mu – z)/sigma)/2 – (-mu + z)/(2*sigma))/(2*sqrt(pi)*sigma)

Example #2 :

 `# Import sympy and Moyal``from` `sympy.stats ``import` `Moyal, density, cdf``from` `sympy ``import` `Symbol, pprint`` ` `z ``=` `Symbol(``"z"``)``mu ``=` `Symbol(``"mu"``, positive ``=` `True``)``sigma ``=` `Symbol(``"sigma"``, positive ``=` `True``)`` ` `# Using sympy.stats.Moyal() method``X ``=` `Moyal(``"x"``, mu, sigma)``Z ``=` `density(X)(z)``gfg ``=` `simplify(cdf(X)(z))`` ` `print``(gfg)`

Output :

1 – erf(sqrt(2)*exp((mu – z)/(2*sigma))/2)

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