Ops

tf.contrib.bayesflow.monte_carlo.expectation(f, p, z=None, n=None, seed=None, name='expectation')

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.BaseDistribution.
  • z: Tensor of samples from p, produced by p.sample_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


tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler(f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler')

Monte Carlo estimate of E_p[f(Z)] = E_q[f(Z) p(Z) / q(Z)].

With p(z) := exp{log_p(z)}, this Op returns

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

This integral is done in log-space with max-subtraction to better handle the often extreme values that f(z) p(z) / q(z) can take on.

If f >= 0, it is up to 2x more efficient to exponentiate the result of expectation_importance_sampler_logspace applied to Log[f].

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

Args:
  • f: Callable mapping samples from sampling_dist_q to Tensors with shape broadcastable to q.batch_shape. For example, f works "just like" q.log_prob.
  • log_p: Callable mapping samples from sampling_dist_q to Tensors with shape broadcastable to q.batch_shape. For example, log_p works "just like" sampling_dist_q.log_prob.
  • sampling_dist_q: The sampling distribution. tf.contrib.distributions.BaseDistribution. float64 dtype recommended. log_p and q should be supported on the same set.
  • z: Tensor of samples from q, produced by q.sample_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:

The importance sampling estimate. Tensor with shape equal to batch shape of q, and dtype = q.dtype.


tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace(log_f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler_logspace')

Importance sampling with a positive function, in log-space.

With p(z) := exp{log_p(z)}, and f(z) = exp{log_f(z)}, this Op returns

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

This integral is done in log-space with max-subtraction to better handle the often extreme values that f(z) p(z) / q(z) can take on.

In contrast to expectation_importance_sampler, this Op returns values in log-space.

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

Args:
  • log_f: Callable mapping samples from sampling_dist_q to Tensors with shape broadcastable to q.batch_shape. For example, log_f works "just like" sampling_dist_q.log_prob.
  • log_p: Callable mapping samples from sampling_dist_q to Tensors with shape broadcastable to q.batch_shape. For example, log_p works "just like" q.log_prob.
  • sampling_dist_q: The sampling distribution. tf.contrib.distributions.BaseDistribution. float64 dtype recommended. log_p and q should be supported on the same set.
  • z: Tensor of samples from q, produced by q.sample_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:

Logarithm of the importance sampling estimate. Tensor with shape equal to batch shape of q, and dtype = q.dtype.