contrib.bayesflow.monte_carlo.expectation

tf.contrib.bayesflow.monte_carlo.expectation

tf.contrib.bayesflow.monte_carlo.expectation

expectation(
    f,
    p,
    z=None,
    n=None,
    seed=None,
    name='expectation'
)

Defined in tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py.

See the guide: BayesFlow Monte Carlo (contrib) > Ops

Monte Carlo estimate of an expectation: E_p[f(Z)] with sample mean.

This Op returns

n^{-1} sum_{i=1}^n f(z_i),  where z_i ~ p
\approx E_p[f(Z)]

User supplies either Tensor of samples z, or number of samples to draw n

Args:

• f: Callable mapping samples from p to Tensors.
• p: tf.contrib.distributions.Distribution.
• z: Tensor of samples from p, produced by p.sample for some n.
• n: Integer Tensor. Number of samples to generate if z is not provided.
• seed: Python integer to seed the random number generator.
• name: A name to give this Op.

Returns:

A Tensor with the same dtype as p.

Example:

N_samples = 10000

distributions = tf.contrib.distributions

dist = distributions.Uniform([0.0, 0.0], [1.0, 2.0])
elementwise_mean = lambda x: x
mean_sum = lambda x: tf.reduce_sum(x, 1)

estimate_elementwise_mean_tf = monte_carlo.expectation(elementwise_mean,
                                                       dist,
                                                       n=N_samples)
estimate_mean_sum_tf = monte_carlo.expectation(mean_sum,
                                               dist,
                                               n=N_samples)

with tf.Session() as sess:
  estimate_elementwise_mean, estimate_mean_sum = (
      sess.run([estimate_elementwise_mean_tf, estimate_mean_sum_tf]))
print estimate_elementwise_mean
>>> np.array([ 0.50018013  1.00097895], dtype=np.float32)
print estimate_mean_sum
>>> 1.49571