sympy.stats.MultivariateT() function in Python

sympy.stats.Multinomial() function in Python

Last Updated : 18 Aug, 2020

With the help of sympy.stats.Multinomial() method, we can create a discrete random variable with Multinomial Distribution.

A multinomial distribution is the probability distribution of the outcomes from a multinomial experiment.

Syntax: sympy.stats.Multinomial(syms, n, p)

Parameters: 
 syms: the symbol
 n: is the number of trials, a positive integer
 p: event probabilites, p>= 0 and p<= 1

Returns: a discrete random variable with Multinomial Distribution

Example #1 :

Python3

# import sympy, Multinomial, density, symbols 
from sympy.stats.joint_rv_types import Multinomial 
from sympy.stats import density 
from sympy import symbols, pprint 
  
x1, x2, x3 = symbols('x1, x2, x3', nonnegative = True, integer = True) 
p1, p2, p3 = symbols('p1, p2, p3', positive = True) 
  
# Using sympy.stats.Multinomial() method 
M = Multinomial('M', 3, p1, p2, p3) 
multiDist = density(M)(x1, x2, x3) 
  
pprint(multiDist)

Output :

/    x1   x2   x3                      
|6*p1  *p2  *p3                        
|----------------  for x1 + x2 + x3 = 3
<  x1!*x2!*x3!                         
|                                      
|       0               otherwise      
\

Example #2 :

Python3

# import sympy, Multinomial, density, symbols 
from sympy.stats.joint_rv_types import Multinomial 
from sympy.stats import density 
from sympy import symbols, pprint 
  
x1, x2, x3 = symbols('x1, x2, x3', nonnegative = True, integer = True) 
  
# Using sympy.stats.Multinomial() method 
M = Multinomial('M', 4, 0, 1, 0) 
multiDist = density(M)(x1, x2, x3) 
  
pprint(multiDist)

Output :

/     x1  x3                      
| 24*0  *0                        
|-----------  for x1 + x2 + x3 = 4
<x1!*x2!*x3!                      
|                                 
|     0            otherwise      
\

sympy.stats.MultivariateT() function in Python

ravikishor

Improve

Article Tags :

Practice Tags :

python

Similar Reads

sympy.stats.NegativeMultinomial() function in Python

With the help of sympy.stats.NegativeMultinomial() method, we can create a discrete random variable with Negative Multinomial Distribution. Syntax: sympy.stats.NegativeMultinomial(syms, k, p) Parameters: syms: the symbol k: number of failures before the experiment is stopped, a positive integer p: e

sympy.stats.MultivariateT() function in Python

With the help of sympy.stats.MultivariateT() method, we can create a joint random variable with multivariate T-distribution. Syntax: sympy.stats.MultivariateT(syms, mu, sigma, v) Parameters: syms: the symbol for identifying the random variable mu: a matrix representing the location vector sigma: The

sympy.stats.NormalGamma() function in Python

With the help of sympy.stats.NormalGamma() method, we can create a bivariate joint random variable with multivariate Normal gamma distribution. Syntax: sympy.stats.NormalGamma(syms, mu, lamda, alpha, beta) Parameters: syms: the symbol, for identifying the random variable mu: a real number, the mean

sympy.stats.MultivariateBeta() function in Python

With the help of sympy.stats.MultivariateBeta() method, we can create a continuous random variable with Dirichlet/Multivariate Beta Distribution. It is a multivariate generalization of the beta distribution. Syntax: sympy.stats.MultivariateBeta(syms, alpha) Parameters: syms: the symbol alpha: positi

sympy.stats.MultivariateEwens() function in Python

With the help of sympy.stats.MultivariateEwens() method, we can create a discrete random variable with Multivariate Ewens Distribution. Syntax: sympy.stats.MultivariateEwens(syms, n, theta) Parameters: syms: the symbol n: size of the sample or the integer whose partitions are considered, a positive

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

sympy.stats.NegativeBinomial() in Python

With the help of sympy.stats.NegativeBinomial() method, we can get the random variable representing the negative binomial distribution. Syntax : sympy.stats.NegativeBinomial(name, r, p) Return : Return the random variable. Example #1 : In this example we can see that by using sympy.stats.NegativeBin

sympy.stats.Skellam() function in Python

With the help of sympy.stats.Skellam() method, we can create a discrete random variable with a Skellam distribution. The Skellam is the distribution of the difference N1 - N2 of two statistically independent random variables N1 and N2 each Poisson-distributed with respective expected values mu1 and

sympy.stats.Moyal() in python

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 tha

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,