  In the field of multi-messenger astronomy, Bayesian inference is commonly
adopted to compare the compatibility of models given the observed data.
However, to describe a physical system like neutron star mergers and their
associated gamma-ray burst (GRB) events, usually more than ten physical
parameters are incorporated in the model. With such a complex model, likelihood
evaluation for each Monte Carlo sampling point becomes a massive task and
requires a significant amount of computational power. In this work, we perform
quick parameter estimation on simulated GRB X-ray light curves using an
interpolated physical GRB model. This is achieved by generating a grid of GRB
afterglow light curves across the parameter space and replacing the likelihood
with a simple interpolation function in the high-dimensional grid that stores
all light curves. This framework, compared to the original method, leads to a
$\sim$90$\times$ speedup per likelihood estimation. It will allow us to explore
different jet models and enable fast model comparison in the future.
