zhusuan.evaluation¶

is_loglikelihood
(meta_bn, observed, latent=None, axis=None, proposal=None)¶ Marginal log likelihood (\(\log p(x)\)) estimates using selfnormalized importance sampling.
Parameters:  meta_bn – A
MetaBayesianNet
instance or a log joint probability function. For the latter, it must accepts a dictionary argument of(string, Tensor)
pairs, which are mappings from all node names in the model to their observed values. The function should return a Tensor, representing the log joint likelihood of the model.  observed – A dictionary of
(string, Tensor)
pairs. Mapping from names of observed stochastic nodes to their values.  latent – A dictionary of
(string, (Tensor, Tensor))
pairs. Mapping from names of latent stochastic nodes to their samples and log probabilities. latent and proposal are mutually exclusive.  axis – The sample dimension(s) to reduce when computing the
outer expectation in the objective. If
None
, no dimension is reduced.  proposal – A
BayesianNet
instance that defines the proposal distributions of latent nodes. proposal and latent are mutually exclusive.
Returns: A Tensor. The estimated log likelihood of observed data.
 meta_bn – A