contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace
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 fromsampling_dist_q
toTensors
with shape broadcastable toq.batch_shape
. For example,log_f
works "just like"sampling_dist_q.log_prob
. -
log_p
: Callable mapping samples fromsampling_dist_q
toTensors
with shape broadcastable toq.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
andq
should be supported on the same set. -
z
:Tensor
of samples fromq
, produced byq.sample
for somen
. -
n
: IntegerTensor
. Number of samples to generate ifz
is not provided. -
seed
: Python integer to seed the random number generator. -
name
: A name to give thisOp
.
Returns:
Logarithm of the importance sampling estimate. Tensor
with shape
equal to batch shape of q
, and dtype
= q.dtype
.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/bayesflow/monte_carlo/expectation_importance_sampler_logspace