est_centroid_one = mc.Uniform('est_centroid_one', 5000, 5050)
est_centroid_two = mc.Uniform('est_centroid_two', 5000, 5050)
est_sigma_one = mc.Uniform('est_sigma_one', 0, 5)
est_sigma_two = mc.Uniform('est_sigma_two', 0, 5)
est_height_one = mc.Uniform('est_height_one', 0, 5)
est_height_two = mc.Uniform('est_height_two', 0, 5)
precision = 1.0 / mc.Uniform('std', 0, 1) ** 2

@mc.deterministic(trace=False)
def est_profile_1(x=wavelength, centroid=est_centroid_one, sigma=
    est_sigma_one, height=est_height_one):
    return GaussFunc(x, height, centroid, sigma)

@mc.deterministic(trace=False)
def est_profile_2(x=wavelength, centroid=est_centroid_two, sigma=
    est_sigma_two, height=est_height_two):
    return GaussFunc(x, height, centroid, sigma)

@mc.deterministic(trace=False)
def mean(profile_1=est_profile_1, profile_2=est_profile_2):
    return profile_1 + profile_2
observations = mc.Normal('obs', mean, precision, value=combined, observed=True)
model = mc.Model([est_centroid_one, est_centroid_two, est_height_one,
    est_height_two, est_sigma_one, est_sigma_two, precision])
map_ = mc.MAP(model)
map_.fit()
mcmc = mc.MCMC(model)
mcmc.sample(50000, 40000)
Сопоставьте два нормальных распределения (гистограммы) с MCMC, используя pymc?