add examples

examples/normal_normal.py

@@ -11,17 +11,20 @@
 
 ed.set_seed(42)
 
-# Normal-Normal with known variance
+# DATA
+x_data = np.array([0.0] * 50, dtype=np.float32)
+
+# MODEL: Normal-Normal with known variance
 mu = Normal(mu=0.0, sigma=1.0)
 x = Normal(mu=tf.ones(50) * mu, sigma=1.0)
 
+# INFERENCE
 qmu_mu = tf.Variable(tf.random_normal([]))
 qmu_sigma = tf.nn.softplus(tf.Variable(tf.random_normal([])))
 qmu = Normal(mu=qmu_mu, sigma=qmu_sigma)
 
-data = {x: np.array([0.0] * 50, dtype=np.float32)}
-
 # analytic solution: N(mu=0.0, sigma=\sqrt{1/51}=0.140)
+data = {x: x_data}
 inference = ed.MFVI({mu: qmu}, data)
 
 inference.initialize()

examples/normal_normal_mh.py

@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+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 Empirical, Normal
+
+ed.set_seed(42)
+
+# DATA
+x_data = np.array([0.0] * 50, dtype=np.float32)
+
+# MODEL: Normal-Normal with known variance
+mu = Normal(mu=0.0, sigma=1.0)
+x = Normal(mu=tf.ones(50) * mu, sigma=1.0)
+
+# INFERENCE
+qmu_params = tf.Variable(tf.zeros([500]))
+qmu = Empirical(params=qmu_params)
+
+proposal_mu = Normal(mu=0.0, sigma=tf.sqrt(1.0 / 51.0))
+
+# analytic solution: N(mu=0.0, sigma=\sqrt{1/51}=0.140)
+data = {x: x_data}
+inference = ed.MetropolisHastings({mu: qmu}, {mu: proposal_mu}, data)
+
+inference.initialize()
+for t in range(inference.n_iter):
+  info_dict = inference.update()
+  inference.print_progress(t, info_dict)
+
+# Check convergence with visual diagnostics.
+sess = ed.get_session()
+samples = sess.run(qmu_params)
+
+# Plot histogram.
+plt.hist(samples, bins='auto')
+plt.show()
+
+# Trace plot.
+plt.plot(samples)
+plt.show()