Beta-Bernoulli.py

#!/usr/bin/env python
"""A simple coin flipping example. Inspired by Stan's toy example.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import edward as ed
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf

from edward.models import Bernoulli, Beta, Empirical

ed.set_seed(42)

# DATA
x_data = np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 1])

# MODEL
p = Beta(1.0, 1.0)
x = Bernoulli(probs=p, sample_shape=10)

# INFERENCE
qp = Empirical(params=tf.Variable(tf.zeros([1000]) + 0.5))

proposal_p = Beta(3.0, 9.0)

inference = ed.MetropolisHastings({p: qp}, {p: proposal_p}, data={x: x_data})
inference.run()

# CRITICISM
# exact posterior has mean 0.25 and std 0.12
sess = ed.get_session()

writer = tf.summary.FileWriter('./log／beta_bernoulli', sess.graph)
mean, stddev = sess.run([qp.mean(), qp.stddev()])
writer.close()

print("Inferred posterior mean:")
print(mean)
print("Inferred posterior stddev:")
print(stddev)

x_post = ed.copy(x, {p: qp})
tx_rep, tx = ed.ppc(
    lambda xs, zs: tf.reduce_mean(tf.cast(xs[x_post], tf.float32)),
    data={x_post: x_data})
ed.ppc_stat_hist_plot(
    tx[0], tx_rep, stat_name=r'$T \equiv$mean', bins=10)
plt.show(block=True)