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
