Machine learning 变分贝叶斯方法——后验分布的不合理结果

我试图实现中的基本示例，以便找到输入数据（我在开始时生成）的均值和方差的后验分布

我的理解是，近似的后验值应该是q（mu）*q（tau）的乘积，所以我想我可以通过简单地乘以网格中每个点的每个分布，然后绘制它来得到它。虽然我看不出我的分布有任何错误，但是伽马分布产生的值非常小，而高斯分布只有一个非零元素。我的猜测是，最后有点不对劲，我将网格中每个点的两个分布乘以由meshgrid生成的网格，但我只是直接从Wikipedia编写了这两个分布。为什么我的后验概率如此之小/nan，我能做些什么来修正它

这是我的密码：

# First Exact solution 
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from scipy.special import factorial

# Produce N data points from known gaussian distribution
real_mu = 2
real_tau = 1
x = np.arange(-3,7,0.1)
N = len(x)
nrv = stats.norm.pdf(x, real_mu, real_tau)

## Approximate posterior distribution over mean and covariance ##
x = nrv     # N data points from "unknown distribution"
N = len(x) # 


# The Algorithm 

#hyperparameters - can be set arbitrarily but smaller positive values indicates larger prior distributions over mu and tau
lambda_0 = 0.05
mu_0 = 0.05
a_0 = 0.05
b_0 = 0.05
## 
x_sum = np.sum(x)
x_ave = np.sum(x)/N
x_squared = np.dot(x, x)
mu_n = (lambda_0*mu_0 + N*x_ave)/(lambda_0 + N)
a_N = a_0 + (N + 1)/2
lambda_N = 1 # Initialize lambda to some arbitrary value
difference = 9999999
while difference > 0.0000001:
    b_N = b_0 + 0.5*((lambda_0 + N)*((1/lambda_N) + mu_n**2) - 2*(lambda_0*mu_0 + x_sum)*mu_n + (x_squared) + lambda_0*mu_0*mu_0)
    new_lambda_N = (lambda_0 + N)*a_N/b_N
    difference_1 = new_lambda_N - lambda_N
    lambda_N = new_lambda_N
    difference = np.absolute(difference_1)


# Calulate the approximated posterior from these parameters: q(mu, tau) = q(mu)q(tau)
t = np.arange(-2,2,0.01)
#qmu = stats.norm.pdf(t, mu_n, 1/lambda_N)
#qtau = gamma.pdf(t, a_N, loc=0, scale=b_N) #scale=1/b_N)
def gaussian(x):
    return (1/(np.sqrt(2*np.pi*sigma*sigma)))*np.exp(-(x-mu_n)*(x-mu_n)/(2*sigma*sigma)) 

def gamma(x):
     return ((b_N**a_N)*(x**(a_N-1))*np.exp(-x*b_N))/(factorial(a_N-1))

sigma = 1/lambda_N

xx, yy = np.meshgrid(t, t)
# First part in zz is from Gaussian distribution over mu and second a gamma distribution over tau
# Same as the two defined functions above
zz = ((1/(np.sqrt(2*np.pi*sigma*sigma)))*np.exp(-(xx-mu_n)*(xx-mu_n)/(2*sigma*sigma)))*((b_N**a_N)*(yy**(a_N-1))*np.exp(-yy*b_N))/(factorial(a_N-1))  

plt.xlabel("mu")
plt.ylabel("tau")
plt.contourf(t,t,zz)
plt.show()