sympy.stats.Logistic() in python Last Updated : 05 Jun, 2020 Comments Improve Suggest changes Like Article Like Report With the help of sympy.stats.Logistic() method, we can get the continuous random variable which represents the logistic distribution. Syntax : sympy.stats.Logistic(name, mu, s) Where, mu and s are real number and mu, s > 0. Return : Return the continuous random variable. Example #1 : In this example we can see that by using sympy.stats.Logistic() method, we are able to get the continuous random variable representing logistic distribution by using this method. Python3 1=1 # Import sympy and Logistic from sympy.stats import Logistic, density from sympy import Symbol, pprint z = Symbol("z") mu = Symbol("mu", positive = True) s = Symbol("s", positive = True) # Using sympy.stats.Logistic() method X = Logistic("x", mu, s) gfg = density(X)(z) pprint(gfg) Output : mu - z ------ s e ---------------- 2 / mu - z \ | ------ | | s | s*\e + 1/ Example #2 : Python3 1=1 # Import sympy and Logistic from sympy.stats import Logistic, density from sympy import Symbol, pprint z = 0.3 mu = 5 s = 1.3 # Using sympy.stats.Logistic() method X = Logistic("x", mu, s) gfg = density(X)(z) pprint(gfg) Output : 0.0196269669241977 Comment More infoAdvertise with us Next Article sympy.stats.Logistic() in python J jitender_1998 Follow Improve Article Tags : Python SymPy Python SymPy-Stats Practice Tags : python Similar Reads sympy.stats.LogLogistic() in python With the help of sympy.stats.LogLogistic() method, we can get the continuous random variable which represents the Log-Logistic distribution. Syntax : sympy.stats.LogLogistic(name, alpha, beta) Where, alpha and beta are real number and alpha, beta > 0. Return : Return the continuous random variabl 1 min read sympy.stats.Logarithmic() in Python With the help of sympy.stats.Logarithmic() method, we can get the random variable representing the logarithmic distribution. Syntax : sympy.stats.Logarithmic(name, p) Return : Return the random variable. Example #1 : In this example we can see that by using sympy.stats.Logarithmic() method, we are a 1 min read sympy.stats.LogNormal() in python With the help of sympy.stats.LogNormal() method, we can get the continuous random variable which represents the Log-Normal distribution. Syntax : sympy.stats.LogNormal(name, mean, std) Where, mean and standard deviation are real number. Return : Return the continuous random variable. Example #1 : In 1 min read sympy.stats.Benini() in Python 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 2 min read sympy.stats.PowerFunction() in Python With the help of sympy.stats.PowerFunction() method, we can get the continuous random variable which represents the Power Function distribution. Syntax : sympy.stats.PowerFunction(name, alpha, a, b) Where, a, b and alpha are real number. Return : Return the continuous random variable. Example #1 : I 1 min read Like