Stochastic Variational Inference(SVI).
Variational inference casts the inference problem as an optimization. Some distributions over the hidden variables are indexed by a set of free parameters, which are optimized to make distributions closest to the posterior of interest. For more details, refer to Variational Inference: A Review for Statisticians.
- Supported Platforms:
numpy.dtype, the loss after training.
Optimize the parameters by training the probability network, and return the trained network.
epochs (int) – Total number of iterations on the data. Default: 10.
train_dataset (Dataset) – A training dataset iterator.
Cell, the trained probability network.