Fit Flow with Girsanov-Tilted Likelihood

Applies a Girsanov change of measure to tilt the likelihood and then fits a flow-based variational posterior using fitflowvariational().

Usage

fitflow_girsanov(
  observed,
  states = NULL,
  flowtype = "maf",
  flowspec = list(),
  inittheta = NULL,
  base_pxgivenz,
  theta_path,
  Winc,
  dt,
  nmc = 256,
  control = list()
)

Arguments

observed

Empirical distribution Q (probability vector).

states

Optional category names.

flowtype

Flow type ("maf", "splinepwlin", "planar", "radial").

flowspec

Structural parameters for the flow.

inittheta

Optional initial theta for trainable flows.

base_pxgivenz

Likelihood p(x|z) before tilting.

theta_path

Drift-tilting function or vector for Girsanov.

Winc

Brownian increments.

dt

Time step.

nmc

Monte Carlo samples.

control

Control list for optim().

Value

Output of fitflowvariational().

Details

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.