  Semi-numerical models of reionization typically involve a large number of
unknown parameters whose values are constrained by comparing with observations.
Increasingly often, exploring this parameter space using semi-numerical
simulations can become computationally intensive, thus necessitating the use of
emulators. In this work, we present a likelihood emulator based on Gaussian
Process Regression (GPR) for our semi-numerical reionization code, SCRIPT, and
use it for parameter inference using mock 21 cm power spectrum data and
Bayesian MCMC analysis. A unique aspect of our methodology is the utilization
of coarse resolution simulations to identify high-probability regions within
the parameter space, employing only a moderate amount of computational time.
Samples drawn from these high-probability regions are used to construct the
training set for the emulator. The subsequent MCMC using this GPR-trained
emulator is found to provide parameter posteriors that agree reasonably well
with those obtained using conventional MCMC. The computing time for the
analysis, which includes both generation of training sets and training the
emulator, is reduced by approximately an order of magnitude. This methodology
is particularly advantageous in scenarios where one wants to use different
parametrizations of reionization models and/or needs to start with broad prior
distributions on the parameters, offering an efficient and effective means of
parameter inference.
