tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace

tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace

expectation_importance_sampler_logspace(
    log_f,
    log_p,
    sampling_dist_q,
    z=None,
    n=None,
    seed=None,
    name='expectation_importance_sampler_logspace'
)

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

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

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.Distribution. float64 dtype recommended. log_p and q should be supported on the same set.
  • z: Tensor of samples from q, produced by q.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:

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