def __init__(self, latent_vars, proposal_vars=None, data=None):
"""
Parameters
----------
proposal_vars : dict of RandomVariable to RandomVariable, optional
Collection of random variables to perform inference on; each is
binded to its complete conditionals which Gibbs cycles draws on.
If not specified, default is to use ``ed.complete_conditional``.
Examples
--------
>>> x_data = np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 1])
>>>
>>> p = Beta(1.0, 1.0)
>>> x = Bernoulli(p=p, sample_shape=10)
>>>
>>> qp = Empirical(tf.Variable(tf.zeros(500)))
>>> inference = ed.Gibbs({p: qp}, data={x: x_data})
"""
if proposal_vars is None:
proposal_vars = {
z: complete_conditional(z)
for z in six.iterkeys(latent_vars)
}
else:
check_latent_vars(proposal_vars)
self.proposal_vars = proposal_vars
super(Gibbs, self).__init__(latent_vars, data)
def __init__(self, latent_vars, proposal_vars=None, data=None):
"""Create an inference algorithm.
Args:
proposal_vars: dict of RandomVariable to RandomVariable, optional.
Collection of random variables to perform inference on; each is
binded to its complete conditionals which Gibbs cycles draws on.
If not specified, default is to use `ed.complete_conditional`.
"""
if proposal_vars is None:
proposal_vars = {z: complete_conditional(z)
for z in six.iterkeys(latent_vars)}
else:
check_latent_vars(proposal_vars)
self.proposal_vars = proposal_vars
super(Gibbs, self).__init__(latent_vars, data)