  The future space based gravitational wave detector LISA (Laser Interferometer
Space Antenna) will observe millions of Galactic binaries constantly present in
the data stream. A small fraction of this population (of the order of several
thousand) will be individually resolved. One of the challenging tasks from the
data analysis point of view will be to estimate the parameters of resolvable
galactic binaries while disentangling them from each other and from other
gravitational wave sources present in the data. This problem is quite often
referred to as a global fit in the field of LISA data analysis. A Bayesian
framework is often used to infer the parameters of the sources and their
number. The efficiency of the sampling techniques strongly depends on the
proposals, especially in the multi-dimensional parameter space. In this paper
we demonstrate how we can use neural density estimators, and in particular
Normalising flows, in order to build proposals which significantly improve the
convergence of sampling. We also demonstrate how these methods could help in
building priors based on physical models and provide an alternative way to
represent the catalogue of identified gravitational wave sources.
