  The halo occupation distribution (HOD) framework is an empirical method to
describe the connection between dark matter halos and galaxies, which is
constrained by small scale clustering data. Efficient fitting procedures are
required to scan the HOD parameter space. This paper describes such a method
based on Gaussian Processes to iteratively build a surrogate model of the
posterior of the likelihood surface from a reasonable amount of likelihood
computations, typically two orders of magnitude less than standard Monte Carlo
Markov chain algorithms. Errors in the likelihood computation due to stochastic
HOD modelling are also accounted for in the method we propose. We report
results of reproducibility, accuracy and stability tests of the method derived
from simulation, taking as a test case star-forming emission line galaxies,
which constitute the main tracer of the Dark Energy Spectroscopic Instrument
and have so far a poorly constrained galaxy-halo connection from observational
data.
