def test_joint_mean_var_sampler():
'''run the gibbs sampler on (mu, var) of the gaussian conditioned on data and plots'''
mean, scale, size = 0, 1, 1000
data = generate_data(mean, scale, size)
model = Gaussian_Model(data)
model.gibbs_sample(num_samples=20000)
mus = model.get_mus(15000, 10)
vars = model.get_vars(15000, 10)
plt.hist(mus)
plt.hist(vars)
plt.show()
def test_mu_sampler():
'''tests an exact sampler of the mu of the gaussian conditioned on data and known var'''
mean, scale, size = 0, 1, 10000
data = generate_data(mean, scale, size)
model = Gaussian_Model(data)
mus = []
for num in xrange(size):
mus.append(model.sample_mean(np.mean(data), float(10^5), float(1), size))
error = abs(np.mean(means)-mean)
print error
plt.hist(means, bins=1000)
plt.show()
def test_var_sampler():
'''tests an exact sampler of the var of the gaussian conditioned on data and know mu'''
mean, scale, size = 0, 1, 10000
data = generate_data(mean, scale, size)
model = Gaussian_Model(data)
vars = []
for num in xrange(size):
vars.append(model.sample_var(0))
error = abs(np.mean(vars)-scale*scale)
print error
plt.hist(vars, bins=1000)
plt.show()