  All-sky searches for continuous gravitational waves are generally model
dependent and computationally costly to run. By contrast, SOAP is a
model-agnostic search that rapidly returns candidate signal tracks in the
time-frequency plane. In this work we extend the SOAP search to return broad
Bayesian posteriors on the astrophysical parameters of a specific signal model.
These constraints drastically reduce the volume of parameter space that any
follow-up search needs to explore, so increasing the speed at which candidates
can be identified and confirmed. Our method uses a machine learning technique,
specifically a conditional variational auto-encoder, and delivers a rapid
estimation of the posterior distribution of the four Doppler parameters of a
continuous wave signal. It does so without requiring a clear definition of a
likelihood function, or being shown any true Bayesian posteriors in training.
We demonstrate how the Doppler parameter space volume can be reduced by a
factor of $\mathcal{O}(10^{-7})$ for signals of SNR 100.
