  Gamma-Ray Bursts (GRBs), being observed at high redshift (z = 9.4), vital to
cosmological studies and investigating Population III stars. To tackle these
studies, we need correlations among relevant GRB variables with the requirement
of small uncertainties on their variables. Thus, we must have good coverage of
GRB light curves (LCs). However, gaps in the LC hinder the precise
determination of GRB properties and are often unavoidable. Therefore, extensive
categorization of GRB LCs remains a hurdle. We address LC gaps using a
'stochastic reconstruction,' wherein we fit two pre-existing models (Willingale
2007; W07 and Broken Power Law; BPL) to the observed LC, then use the
distribution of flux residuals from the original data to generate data to fill
in the temporal gaps. We also demonstrate a model-independent LC reconstruction
via Gaussian Processes. At 10% noise, the uncertainty of the end time of the
plateau, its correspondent flux, and the temporal decay index after the plateau
decreases, on average, by 33.3% 35.03%, and 43.32%, respectively for the W07,
and by 33.3%, 30.78%, 43.9% for the BPL. The slope of the plateau decreases by
14.76% in the BPL. After using the Gaussian Process technique, we see similar
trends of a decrease in uncertainty for all model parameters for both the W07
and BPL models. These improvements are essential for the application of GRBs as
standard candles in cosmology, for the investigation of theoretical models and
for inferring the redshift of GRBs with future machine learning analysis.
