tf.contrib.distributions.Binomial.prob(value, name='prob') Probability density/mass function (depending on is_continuous)

tf.contrib.distributions.StudentT.prob(value, name='prob') Probability density/mass function (depending on is_continuous)

tf.contrib.learn.monitors.LoggingTrainable.step_begin(step) Overrides BaseMonitor.step_begin. When

tf.contrib.distributions.LaplaceWithSoftplusScale.validate_args Python boolean indicated possibly expensive checks are enabled

tf.contrib.learn.monitors.RunHookAdapterForMonitors.after_run(run_context, run_values)

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.graph

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.entropy.entropy_shannon(p, z=None, n=None, seed=None, form=None, name='entropy_shannon') Monte Carlo or deterministic

tf.contrib.distributions.StudentT.allow_nan_stats Python boolean describing behavior when a stat is undefined.