# 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 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       ```

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