esem.sampler.Sampler.sample¶
- Sampler.sample(prior_x=None, n_samples=1)¶
This is the call that does the actual inference.
It should call model.sample over the prior, compare with the objective, and then output samples from the posterior distribution
- Parameters
prior_x (
tensorflow_probability.distribution) – The distribution to sample parameters from. By default it will uniformly sample the unit N-D hypercuben_samples (
int) – The number of samples to draw
- Returns
np.array– Array of samples