class_docstr: The logit-normal distribution.
class_docstr_tok: ["The","logit-normal","distribution","."]
docstr: Approximates `E_Normal(m,s)[ Bernoulli(sigmoid(X)).log_prob(Y) ]`.
  
  This approximation is based on combining ideas from
  [(Monahan and Stefanski, 1989)][1] and [(Owen, 1980)][2].
  
  Warning: usual numerical guarantees are not offered for this function as it
  attempts to strike a balance between computational cost, implementation
  simplicity and numerical accuracy.
  
  Args:
    y: The events over which to compute the Bernoulli log prob.
      Default value: `None` (i.e., `1`).
    name: Python `str` prepended to names of ops created by this function.
      Default value: `'mean_log_prob_approx'`.
  
  Returns:
    mean_log_prob_approx: An approximation of the mean of the Bernoulli
      likelihood.
  
  #### References
  
  [1]: Monahan, John H., and Leonard A. Stefanski. Normal scale mixture
       approximations to the logistic distribution with applications. North
       Carolina State University. Dept. of Statistics, 1989.
       http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.5032
  [2]: Owen, Donald Bruce. "A table of normal integrals: A table."
       Communications in Statistics-Simulation and Computation 9.4 (1980):
       389-419.
       https://www.tandfonline.com/doi/abs/10.1080/03610918008812164
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code: def mean_log_prob_approx(self, y=None, name="mean_log_prob_approx"):
      """Approximates `E_Normal(m,s)[ Bernoulli(sigmoid(X)).log_prob(Y) ]`.
  
      This approximation is based on combining ideas from
      [(Monahan and Stefanski, 1989)][1] and [(Owen, 1980)][2].
  
      Warning: usual numerical guarantees are not offered for this function as it
      attempts to strike a balance between computational cost, implementation
      simplicity and numerical accuracy.
  
      Args:
        y: The events over which to compute the Bernoulli log prob.
          Default value: `None` (i.e., `1`).
        name: Python `str` prepended to names of ops created by this function.
          Default value: `'mean_log_prob_approx'`.
  
      Returns:
        mean_log_prob_approx: An approximation of the mean of the Bernoulli
          likelihood.
  
      #### References
  
      [1]: Monahan, John H., and Leonard A. Stefanski. Normal scale mixture
           approximations to the logistic distribution with applications. North
           Carolina State University. Dept. of Statistics, 1989.
           http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.5032
      [2]: Owen, Donald Bruce. "A table of normal integrals: A table."
           Communications in Statistics-Simulation and Computation 9.4 (1980):
           389-419.
           https://www.tandfonline.com/doi/abs/10.1080/03610918008812164
      """
      with self._name_and_control_scope(name):
          return approx_expected_log_prob_sigmoid(
              self.loc,
              self.scale,
              y,
              MONAHAN_MIX_PROB[self.num_probit_terms_approx],
              MONAHAN_INVERSE_SCALE[self.num_probit_terms_approx],
          )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 8 for calling the LogitNormal obj's underlying member method code with example usage: obj.mean_log_prob_approx(y, name) and returns: approx_expected_log_prob_sigmoid