Created
May 22, 2016 12:07
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Bayse Fitting Sample
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import math | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import matplotlib.mlab as mlab | |
I = 100 | |
data = np.random.normal(0, 2, I) | |
y = np.ones(I) | |
plt.hold(False) | |
plt.plot(data, y, "o") | |
plt.savefig("data.png") | |
mean = np.mean(data) | |
var = np.var(data) | |
alpha = 1.0 | |
beta = 1.0 | |
gamma = 1.0 | |
delta = 0.0 | |
lin = np.linspace(-5, 5, 200) | |
norm = mlab.normpdf(lin, 0, 2) | |
plt.plot(lin, norm, "red") | |
plt.hold(True) | |
norm = mlab.normpdf(lin, mean, math.sqrt(var)) | |
plt.plot(lin, norm, "blue") | |
mu_hat = (I * mean + gamma * delta) / (I + gamma) | |
norm = mlab.normpdf(lin, mu_hat, math.sqrt((I * var + 2 * beta + gamma * (delta - mu_hat) ** 2) / (I + 3 + 2 * alpha))) | |
plt.plot(lin, norm, "green") | |
plt.savefig("graph.png") | |
plt.show() | |
plt.hold(False) |
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