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 of the normal distribution lambda: a positive integer alpha: a positive integer beta: a positive integer Returns: a bivariate joint random variable with multivariate Normal gamma distribution.
Example #1 :
Python3
# import sympy, NormalGamma, density, symbols from sympy.stats import density, NormalGamma from sympy import symbols, pprint y, z = symbols( 'y z' ) # using sympy.stats.NormalGamma() method X = NormalGamma( 'X' , 0 , 1 , 2 , 3 ) norGammaDist = density(X)(y, z) pprint(norGammaDist) |
Output :
2 -y *z ------ ___ 3/2 -3*z 2 9*\/ 2 *z *e *e -------------------------- ____ 2*\/ pi
Example #2 :
Python3
# import sympy, NormalGamma, density, symbols from sympy.stats import density, NormalGamma from sympy import symbols, pprint y, z = symbols( 'y z' ) # using sympy.stats.NormalGamma() method X = NormalGamma( 'X' , 1 / 2 , 3 , 4 , 6 ) norGammaDist = density(X)(y, z) pprint(norGammaDist) |
Output :
2 -3*z*(y - 1/2) ---------------- ___ 7/2 -6*z 2 108*\/ 6 *z *e *e -------------------------------------- ____ \/ pi