  Monte Carlo simulations are a crucial component when analysing the Standard
Model and New physics processes at the Large Hadron Collider. This paper aims
to explore the performance of generative models for complementing the
statistics of classical Monte Carlo simulations in the final stage of data
analysis by generating additional synthetic data that follows the same
kinematic distributions for a limited set of analysis-specific observables to a
high precision. Several deep generative models are adapted for this task and
their performance is systematically evaluated using a well-known benchmark
sample containing the Higgs boson production beyond the Standard Model and the
corresponding irreducible background. The paper evaluates the autoregressive
models and normalizing flows and the applicability of these models using
different model configurations is investigated. The best performing model is
chosen for a further evaluation using a set of statistical procedures and a
simplified physics analysis. By implementing and performing a series of
statistical tests and evaluations we show that a machine-learning-based
generative procedure can be used to generate synthetic data that matches the
original samples closely enough and that it can therefore be incorporated in
the final stage of a physics analysis with some given systematic uncertainty.
