Defined in edward/util/__init__.py
.
check_data(...)
: Check that the data dictionary passed during inference and
check_latent_vars(...)
: Check that the latent variable dictionary passed during inference and
compute_multinomial_mode(...)
: Compute the mode of a Multinomial random variable.
copy(...)
: Build a new node in the TensorFlow graph from org_instance
,
dot(...)
: Compute dot product between a 2-D tensor and a 1-D tensor.
get_ancestors(...)
: Get ancestor random variables of input.
get_blanket(...)
: Get Markov blanket of input, which consists of its parents, its
get_children(...)
: Get child random variables of input.
get_control_variate_coef(...)
: Returns scalar used by control variates method for variance reduction in
get_descendants(...)
: Get descendant random variables of input.
get_parents(...)
: Get parent random variables of input.
get_session(...)
: Get the globally defined TensorFlow session.
get_siblings(...)
: Get sibling random variables of input.
get_variables(...)
: Get parent TensorFlow variables of input.
is_independent(...)
: Assess whether a is independent of b given the random variables in
random_variables(...)
: Return all random variables in the TensorFlow graph.
rbf(...)
: Radial basis function kernel, also known as the squared
set_seed(...)
: Set seed for both NumPy and TensorFlow.
to_simplex(...)
: Transform real vector of length (K-1)
to a simplex of dimension K
transform(...)
: Transform a continuous random variable to the unconstrained space.
with_binary_averaging(...)
: Inspired by scikit-learn’s _average_binary_score function: