  In this work, we demonstrate that applying deep generative machine learning
models for lattice field theory is a promising route for solving problems where
Markov Chain Monte Carlo (MCMC) methods are problematic. More specifically, we
show that generative models can be used to estimate the absolute value of the
free energy, which is in contrast to existing MCMC-based methods which are
limited to only estimate free energy differences. We demonstrate the
effectiveness of the proposed method for two-dimensional $\phi^4$ theory and
compare it to MCMC-based methods in detailed numerical experiments.
