tensorflow_probability/python/internal/monte_carlo.py
def _logspace_mean(log_values):
  """Evaluate `Log[E[values]]` in a stable manner.

  Args:
    log_values:  `Tensor` holding `Log[values]`.

  Returns:
    `Tensor` of same `dtype` as `log_values`, reduced across dim 0.
      `Log[Mean[values]]`.
  """
  # center = Max[Log[values]],  with stop-gradient
  # The center hopefully keep the exponentiated term small.  It is canceled
  # from the final result, so putting stop gradient on it will not change the
  # final result.  We put stop gradient on to eliminate unnecessary computation.
  center = tf.stop_gradient(_sample_max(log_values))

  # centered_values = exp{Log[values] - E[Log[values]]}
  centered_values = tf.math.exp(log_values - center)

  # log_mean_of_values = Log[ E[centered_values] ] + center
  #                    = Log[ E[exp{log_values - E[log_values]}] ] + center
  #                    = Log[E[values]] - E[log_values] + center
  #                    = Log[E[values]]
  log_mean_of_values = tf.math.log(_sample_mean(centered_values)) + center

  return log_mean_of_values
def _logspace_mean(log_values):
  """Evaluate `Log[E[values]]` in a stable manner.

  Args:
    log_values:  `Tensor` holding `Log[values]`.

  Returns:
    `Tensor` of same `dtype` as `log_values`, reduced across dim 0.
      `Log[Mean[values]]`.
  """
  # center = Max[Log[values]],  with stop-gradient
  # The center hopefully keep the exponentiated term small.  It is canceled
  # from the final result, so putting stop gradient on it will not change the
  # final result.  We put stop gradient on to eliminate unnecessary computation.
  center = tf.stop_gradient(_sample_max(log_values))

  # centered_values = exp{Log[values] - E[Log[values]]}
  centered_values = tf.math.exp(log_values - center)

  # log_mean_of_values = Log[ E[centered_values] ] + center
  #                    = Log[ E[exp{log_values - E[log_values]}] ] + center
  #                    = Log[E[values]] - E[log_values] + center
  #                    = Log[E[values]]
  log_mean_of_values = tf.math.log(_sample_mean(centered_values)) + center

  return log_mean_of_values
def _logspace_mean ( log_values ) : # center = Max[Log[values]],  with stop-gradient # The center hopefully keep the exponentiated term small.  It is canceled # from the final result, so putting stop gradient on it will not change the # final result.  We put stop gradient on to eliminate unnecessary computation. center = tf . stop_gradient ( _sample_max ( log_values ) ) # centered_values = exp{Log[values] - E[Log[values]]} centered_values = tf . math . exp ( log_values - center ) # log_mean_of_values = Log[ E[centered_values] ] + center #                    = Log[ E[exp{log_values - E[log_values]}] ] + center #                    = Log[E[values]] - E[log_values] + center #                    = Log[E[values]] log_mean_of_values = tf . math . log ( _sample_mean ( centered_values ) ) + center return log_mean_of_values
Evaluate `Log[E[values]]` in a stable manner.

  Args:
    log_values:  `Tensor` holding `Log[values]`.

  Returns:
    `Tensor` of same `dtype` as `log_values`, reduced across dim 0.
      `Log[Mean[values]]`.
Evaluate Log [ E [ values ]] in a stable manner .