sympy.stats.Benini() in Python Last Updated : 18 Aug, 2021 Comments Improve Suggest changes Like Article Like Report With the help of sympy.stats.Benini() method, we can get the continuous random variable representing the benini distribution. Syntax : sympy.stats.Benini(name, alpha, beta, sigma) Where, alpha, beta and sigma are real number and greater than 0. Return : Return the continuous random variable. Example #1 : In this example, we can see that by using sympy.stats.Benini() method, we are able to get the continuous random variable represents the benini distribution by using this method. Python3 # Import sympy and Benini from sympy.stats import Benini, density, cdf from sympy import Symbol, simplify, pprint alpha = Symbol("alpha", positive = True) beta = Symbol("beta", positive = True) sigma = Symbol("sigma", positive = True) z = Symbol("z") # Using sympy.stats.Benini() method X = Benini("x", alpha, beta, sigma) GFG = density(X)(z) pprint(GFG, use_unicode = False) Output : / / z \\ / z \ 2/ z \ | 2*beta*log|-----|| - alpha*log|-----| - beta*log |-----| |alpha \sigma/| \sigma/ \sigma/ |----- + -----------------|*e \ z z / Example #2 : Python3 # Import sympy and Benini from sympy.stats import Benini, density, cdf from sympy import Symbol, simplify, pprint alpha = 4 beta = 6 sigma = 3 z = 0.2 # Using sympy.stats.Benini() method X = Benini("x", alpha, beta, sigma) GFG = density(X)(z) pprint(GFG, use_unicode = False) Output : -5.60587100451865e-13 Comment More infoAdvertise with us Next Article sympy.stats.Benini() in Python J jitender_1998 Follow Improve Article Tags : Python SymPy Python SymPy-Stats Practice Tags : python Similar Reads sympy.stats.Beta() in Python With the help of sympy.stats.Beta() method, we can get the continuous random variable which represents the beta distribution. Syntax : sympy.stats.Beta(name, alpha, beta) Where, alpha and beta is greater than 0. Return : Return the continuous random variable. Example #1 : In this example we can see 1 min read sympy.stats.Coin() in Python With the help of sympy.stats.Coin() method, we can create a fair or unfair coin by using sympy.stats.Coin() method. A fair coin have the probability of Half but we can't tell for unfair coin. Syntax : sympy.stats.Coin(name, value) Parameters : Name - It stands for the name of the coin. Value - By de 1 min read sympy.stats.Gumbel() in python With the help of sympy.stats.Gumbel() method, we can get the continuous random variable which represents the gumbel distribution. Syntax : sympy.stats.Gumbel(name, beta, mu, minimum=False) Where, If we set minimum is True than we can enable minimum distribution. Return : Return the continuous random 1 min read sympy.stats.BetaPrime() in Python With the help of sympy.stats.BetaPrime() method, we can get the continuous random variable which represents the betaprime distribution. Syntax : sympy.stats.BetaPrime(name, alpha, beta) Return : Return the continuous random variable. Example #1 : In this example we can see that by using sympy.stats. 1 min read sympy.stats.Die() function in Python With the help of sympy.stats.Die() method, we can get the fair die having number of faces given by the parameter and having name defined in the parameter itself by using sympy.stats.Die() method. Syntax : sympy.stats.Die(name, faces) Return : Return a fair die having 'n' faces. Example #1 : In this 1 min read Like