sbi.neural_nets.marginal_nn#
- marginal_nn(model, z_score_x='independent', hidden_features=50, num_transforms=5, num_bins=10, num_components=10, **kwargs)[source]#
Returns a function that builds a density estimator for learning the marginal.
- Parameters:
model (ZukoFlowType) – The type of density estimator that will be created.
z_score_x (Literal['independent', 'structured', 'transform_to_unconstrained', 'none'] | None) – Whether to z-score samples \(x\) before passing them into the network.
hidden_features (int) – Number of hidden features.
num_transforms (int) – Number of transforms when a flow is used.
num_bins (int) – Number of bins used for the splines in nsf.
num_components (int) – Number of mixture components for a mixture of Gaussians.
**kwargs (Any) – Additional estimator arguments. Valid keys are defined by
MarginalFlowConfig; unknown keys trigger a warning and are forwarded to the builder.
- Return type: