sympy.stats.Logarithmic() in Python Last Updated : 08 Jun, 2020 Summarize Comments Improve Suggest changes Share Like Article Like Report 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 able to get the random variable representing the logarithmic distribution by using this method. Python3 1=1 # Import sympy and logarithmic from sympy.stats import Logarithmic, density, E, variance from sympy import Symbol, S p = S.One / 5 # Using sympy.stats.Logarithmic() method X = Logarithmic("x", p) gfg = density(X)(3) print(gfg) Output : -1/(375*log(4/5)) Example #2 : Python3 1=1 # Import sympy and logarithmic from sympy.stats import Logarithmic, density, E, variance from sympy import Symbol, S p = 0.46 # Using sympy.stats.Logarithmic() method X = Logarithmic("x", p) gfg = density(X)(4) print(gfg) Output : 0.0181660042052665 Comment More infoAdvertise with us Next Article sympy.stats.Logarithmic() in Python J jitender_1998 Follow Improve Article Tags : Python SymPy Python SymPy-Stats Practice Tags : python Similar Reads sympy.stats.Logistic() in python 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 exam 1 min read 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.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.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 sympy.stats.Binomial() function in Python With the help of sympy.stats.Binomial() method, we can create a Finite Random Variable representing a binomial distribution. A binomial distribution is the probability of a SUCCESS or FAILURE outcome in an experiment or survey that is repeated multiple times. Syntax: sympy.stats.Binomial(name, n, p 1 min read Like