将pyMC2贝叶斯A / B测试示例移植到pyMC3

我正在努力学习pyMC 3，遇到了一些麻烦。由于pyMC3的教程有限，因此我正在使用贝叶斯黑客方法。我试图在贝叶斯A / B测试示例中将pyMC 2代码移植到pyMC 3，但没有成功。从我可以看到的模型根本没有考虑到观察。

我必须对示例进行一些更改，因为pyMC 3完全不同，因此应如下所示：将pymc导入为pm

# The parameters are the bounds of the Uniform.
p = pm.Uniform('p', lower=0, upper=1)

# set constants
p_true = 0.05  # remember, this is unknown.
N = 1500

# sample N Bernoulli random variables from Ber(0.05).
# each random variable has a 0.05 chance of being a 1.
# this is the data-generation step
occurrences = pm.rbernoulli(p_true, N)

print occurrences  # Remember: Python treats True == 1, and False == 0
print occurrences.sum()

# Occurrences.mean is equal to n/N.
print "What is the observed frequency in Group A? %.4f" % occurrences.mean()
print "Does this equal the true frequency? %s" % (occurrences.mean() == p_true)

# include the observations, which are Bernoulli
obs = pm.Bernoulli("obs", p, value=occurrences, observed=True)

# To be explained in chapter 3
mcmc = pm.MCMC([p, obs])
mcmc.sample(18000, 1000)

figsize(12.5, 4)
plt.title("Posterior distribution of $p_A$, the true effectiveness of site A")
plt.vlines(p_true, 0, 90, linestyle="--", label="true $p_A$ (unknown)")
plt.hist(mcmc.trace("p")[:], bins=25, histtype="stepfilled", normed=True)
plt.legend()

相反，看起来像：

import pymc as pm

import random
import numpy as np
import matplotlib.pyplot as plt

with pm.Model() as model:
    # Prior is uniform: all cases are equally likely
    p = pm.Uniform('p', lower=0, upper=1)

    # set constants
    p_true = 0.05  # remember, this is unknown.
    N = 1500

    # sample N Bernoulli random variables from Ber(0.05).
    # each random variable has a 0.05 chance of being a 1.
    # this is the data-generation step
    occurrences = []  # pm.rbernoulli(p_true, N)
    for i in xrange(N):
        occurrences.append((random.uniform(0.0, 1.0) <= p_true))
    occurrences = np.array(occurrences)
    obs = pm.Bernoulli('obs', p_true, observed=occurrences)

    start = pm.find_MAP()
    step = pm.Metropolis()
    trace = pm.sample(18000, step, start)
    pm.traceplot(trace);
    plt.show()

对于冗长的帖子，我们深表歉意，但在我的适应中，发生了一些小变化，例如，由于pm.rbernoulli不再存在，因此手动生成了观察结果。我也不确定在运行跟踪之前是否应该找到起点。我应该如何更改实现以使其正确运行？