tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler(f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler') Monte Carlo estimate of E_p[f(Z)] = E_q[f(Z) p(Z) / q(Z)]. With p(z) := exp{log_p(z)}, this Op returns n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ], z_i ~ q, \approx E_q[ f(Z) p(Z) / q(Z) ] = 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 o

tf.contrib.distributions.Beta.a_b_sum Sum of parameters.

virtual uint64 tensorflow::Env::NowMicros()=0 Returns the number of micro-seconds since some fixed point in time. Only useful for computing deltas of time.

tf.SparseTensor.eval(feed_dict=None, session=None) Evaluates this sparse tensor in a Session. Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor. N.B. Before invoking SparseTensor.eval(), its graph must have been launched in a session, and either a default session must be available, or session must be specified explicitly. Args: feed_dict: A dictionary that maps Tensor objects to feed values. See Session.run() f

Status tensorflow::Env::DeleteFile(const string &fname) Deletes the named file.

void tensorflow::EnvWrapper::SchedClosureAfter(int64 micros, std::function< void()> closure) override

tf.contrib.distributions.Bernoulli.p

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.name

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.entropy(name='entropy')

tf.contrib.distributions.StudentT.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.