VIPosteriorParameters

VIPosteriorParameters#

class VIPosteriorParameters(q='maf', vi_method='rKL', num_transforms=5, hidden_features=50, z_score_theta='independent', z_score_x='independent')[source]#

Bases: PosteriorParameters

Parameters for VIPosterior, supporting both single-x and amortized VI.

Fields:
q: Variational distribution. Either a string specifying the flow type

[maf, nsf, naf, unaf, nice, sospf, gf, gaussian, gaussian_diag], a Distribution, a VIPosterior object, or a Callable builder function. For amortized VI, only string flow types are supported. If q is already a VIPosterior, arguments are copied from it (relevant for multi-round training). Note: For 1D problems, prefer “gf” (mixture of Gaussians) or “gaussian” as autoregressive flows may be unstable.

vi_method: Variational method for fitting q to the posterior. Options:

[rKL, fKL, IW, alpha]. Some are “mode seeking” (rKL, alpha > 1) and some are “mass covering” (fKL, IW, alpha < 1). Currently only used for single-x VI; amortized VI uses ELBO (rKL).

num_transforms: Number of transforms in the normalizing flow. Used for

both single-x VI (via set_q/train) and amortized VI (via train_amortized).

hidden_features: Hidden layer size in the flow networks. Used for both

single-x VI and amortized VI.

z_score_theta: Method for z-scoring θ (the parameters being sampled).

One of “none”, “independent”, “structured”. Used for both single-x VI and amortized VI. Use “structured” for parameters with correlations.

z_score_x: Method for z-scoring x (the conditioning observation).

One of “none”, “independent”, “structured”. Only used for amortized VI (train_amortized). Use “structured” for structured data like images.

Note

For custom distributions that lack parameters() and modules() methods, pass these via VIPosterior.set_q(q, parameters=…, modules=…) instead.

Parameters:

  • q (Literal['maf', 'nsf', 'naf', 'unaf', 'nice', 'sospf', 'gf', 'gaussian', 'gaussian_diag'] | ~torch.distributions.distribution.Distribution | ~sbi.inference.posteriors.vi_posterior.VIPosterior | ~typing.Callable)

  • vi_method (Literal['rKL', 'fKL', 'IW', 'alpha'])

  • num_transforms (int)

  • hidden_features (int)

  • z_score_theta (Literal['none', 'independent', 'structured'])

  • z_score_x (Literal['none', 'independent', 'structured'])

q: Literal['maf', 'nsf', 'naf', 'unaf', 'nice', 'sospf', 'gf', 'gaussian', 'gaussian_diag'] | Distribution | VIPosterior | Callable = 'maf'#
vi_method: Literal['rKL', 'fKL', 'IW', 'alpha'] = 'rKL'#
num_transforms: int = 5#
hidden_features: int = 50#
z_score_theta: Literal['none', 'independent', 'structured'] = 'independent'#
z_score_x: Literal['none', 'independent', 'structured'] = 'independent'#
validate()[source]#

Validate VIPosteriorParameters fields.

with_param(**kwargs)#

Create a new instance of the class with updated field values.

Only allows updates to fields defined in the dataclass. Raises an error if any unknown or invalid field names are passed.

Parameters:

**kwargs – Field-value pairs to override in the new instance.

Returns:

A new instance of the same class with updated values.

Raises:

ValueError – If any of the provided keys are not valid dataclass fields.

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