Applies a Girsanov change of measure to tilt the likelihood and then
fits a flow-based variational posterior using fitflowvariational().
fitflow_girsanov(
observed,
states = NULL,
flowtype = "maf",
flowspec = list(),
inittheta = NULL,
base_pxgivenz,
theta_path,
Winc,
dt,
nmc = 256,
control = list()
)Output of fitflowvariational().
Empirical distribution Q (probability vector).
Optional category names.
Flow type ("maf", "splinepwlin", "planar", "radial").
Structural parameters for the flow.
Optional initial theta for trainable flows.
Likelihood \(p(x \mid z)\) before tilting.
Drift-tilting function or vector for Girsanov.
Brownian increments.
Time step.
Monte Carlo samples.
Control list for optim().
This is useful when the target distribution arises from a drift-tilted diffusion process, where the Radon-Nikodym derivative is given by the Girsanov theorem.