With the help of sympy.stats.GeneralizedMultivariateLogGammaOmega() method, we can get the continuous joint random variable which represents the extended Generalized Multivariate Log Gamma distribution.
Syntax :
GeneralizedMultivariateLogGammaOmega(syms, omega, v, lamda, mu)
Parameters :
1) Syms – list of symbols
2) Omega – a square matrix
3) V – positive real number
4) Lambda – a list of positive reals
5) mu – a list of positive real numbers.
Return : Return the continuous joint random variable.
Example #1 :
In this example we can see that by using sympy.stats.GeneralizedMultivariateLogGammaOmega() method, we are able to get the continuous joint random variable representing extended Generalized Multivariate Log Gamma distribution by using this method.
# Import sympy and GeneralizedMultivariateLogGammaOmega from sympy.stats import density from sympy.stats.joint_rv_types import GeneralizedMultivariateLogGammaOmega from sympy.stats.joint_rv import marginal_distribution from sympy import symbols, S, Matrix v = 1l, mu = [1, 1, 1], [1, 1, 1] d = S.One y = symbols('y_1:4', positive = True) omega = Matrix([[1, S.Half, S.Half], [S.Half, 1, S.Half], [S.Half, S.Half, 1]]) # Using sympy.stats.GeneralizedMultivariateLogGammaOmega() method Gd = GeneralizedMultivariateLogGammaOmega('G', omega, v, l, mu) gfg = density(Gd)(y[0], y[1], y[2]) pprint(gfg) |
Output :
oo
______
\ `
\ n
\ / ___\ y_1 y_2 y_3
\ | \/ 2 | (n + 1)*(y_1 + y_2 + y_3) - e - e - e
___ \ |1 - -----| *e
\/ 2 * / \ 2 /
/ ------------------------------------------------------------
/ 3
/ Gamma (n + 1)
/_____,
n = 0
--------------------------------------------------------------------------
2
Example #2 :
# Import sympy and GeneralizedMultivariateLogGammaOmega from sympy.stats import density from sympy.stats.joint_rv_types import GeneralizedMultivariateLogGammaOmega from sympy.stats.joint_rv import marginal_distribution from sympy import symbols, S, Matrix v = 1l, mu = [1, 2], [2, 1] d = S.One y = symbols('y_1:3', positive = True) omega = Matrix([[1, S.Half], [S.Half, 1]]) # Using sympy.stats.GeneralizedMultivariateLogGammaOmega() method Gd = GeneralizedMultivariateLogGammaOmega('G', omega, v, l, mu) gfg = density(Gd)(y[0], y[1]) pprint(gfg) |
Output :
oo
______
\ `
\ y_2
\ 2*y_1 e
\ (n + 1)*(2*y_1 + y_2) - e - ----
\ -n - 1 -n 2
3* / 2*2 *4 *e
/ ----------------------------------------------------
/ 2
/ Gamma (n + 1)
/_____,
n = 0
--------------------------------------------------------------
4
