混合高斯在pyMC3

Question

沒有收斂我有3個高斯混合但是不管我多麼調整先驗我不能讓後意味着從他們的前值移動..

k = 3 

n1 = 1000 
n2 = 1000 
n3 = 1000 

n = n1+n2+n3 

mean1 = 17.3 
mean2 = 42.0 
mean3 = 31.0 

precision = 0.1 

sigma = np.sqrt(1/precision) 

print "Standard deviation: %s" % sigma 

data1 = np.random.normal(mean1,sigma,n1) 
data2 = np.random.normal(mean2,sigma,n2) 
data3 = np.random.normal(mean3,sigma,n3) 

data = np.concatenate([data1 , data2, data3]) 

hist(data, bins=200, color="k", histtype="stepfilled", alpha=0.8) 
plt.title("Histogram of the dataset") 
plt.ylim([0, None]) 

with pm.Model() as model: 
    dd = pm.Dirichlet('dd', a=np.array([float(n/k) for i in range(k)]), shape=k) 
    sd = pm.Uniform('precs', lower=1, upper=5, shape=k) 
    means = pm.Normal('means', [25, 30, 35], 0.01, shape=k) 
    category = pm.Categorical('category', p=dd, shape=n) 

    points = pm.Normal('obs', 
        means[category], 
        sd=sd[category], 
        observed=data) 
    tr = pm.sample(100000, step=pm.Metropolis()) 
    pm.traceplot(tr, vars=['means', 'precs', 'dd']) 

輸出：

Standard deviation: 3.16227766017 
[-----------------100%-----------------] 100000 of 100000 complete in 157.2 sec 

正如你可以看到有沒有收斂和手段不從初始值移動

Answer 1

不幸的是，這是一個已知問題：我們正在努力的https://github.com/pymc-devs/pymc/issues/452和https://github.com/pymc-devs/pymc/issues/443。

請注意，還有其他可用於分類的步驟方法，如問題中的示例模型。但即使這樣也不會導致融合。