core.posterior_models.flow_matching.FlowMatchingPosteriorModel

core.posterior_models.flow_matching.FlowMatchingPosteriorModel(**kwargs)

Attributes

Name	Description
eps
sigma_min

Methods

Name	Description
evaluate_vector_field	Evaluate the vector field v(t, theta_t, context_data) that generates the flow
loss	Calculates loss as the mean squared error between the predicted vector field and

evaluate_vector_field

core.posterior_models.flow_matching.FlowMatchingPosteriorModel.evaluate_vector_field(
    t,
    theta_t,
    *context_data,
)

Evaluate the vector field v(t, theta_t, context_data) that generates the flow via the ODE

d/dt f(theta_t, t, context) = v(f(theta_t, t, context), t, context).

For flow matching, the vector field is regressed directly during training.

Parameters

Name	Type	Description	Default
t		time (noise level)	required
theta_t		noisy parameters, perturbed with noise level t	required
*context_data		list with context data (GW data)	()

loss

core.posterior_models.flow_matching.FlowMatchingPosteriorModel.loss(
    theta,
    *context,
)

Calculates loss as the mean squared error between the predicted vector field and the vector field for transporting the parameter data to samples from the prior.

Parameters

Name	Type	Description	Default
theta		Parameter values at which to evaluate the density. Should have a batch dimension (even if size B = 1).	required
context		Context information (typically observed data). Must have the same leading (batch) dimension as theta.	()

Returns

Name	Type	Description
loss	torch.Tensor	Mean loss across the batch (a scalar).