Python Sympy stats.GeneralizedMultivariateLogGammaOmega()用法及代碼示例

借助sympy.stats.GeneralizedMultivariateLogGammaOmega()方法，我們可以獲得連續的關節隨機變量，該變量表示擴展的廣義多元對數伽馬分布。

用法： GeneralizedMultivariateLogGammaOmega(syms, omega, v, lamda, mu)

參數：

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.

返回：Return the continuous joint random variable.

範例1：
在這個例子中，我們可以通過使用sympy.stats.GeneralizedMultivariateLogGammaOmega()該方法可以得到代表擴展廣義廣義對數伽馬分布的連續聯合隨機變量。

# 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 = 1
l, 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)

輸出：

         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                                     

範例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 = 1
l, 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)

輸出：

     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