  The 21cm global signal is an important probe to reveal the properties of the
first astrophysical objects and the processes of the structure formation from
which one can constrain astrophysical and cosmological parameters. To extract
the information of such parameters, one needs to efficiently evaluate the 21cm
global signal for statistical analysis. First we developed an artificial neural
network-based emulator to predict the 21cm global signal, which works with
significantly less computational cost and high precision. Then we apply our
emulator to demonstrate the parameter estimation based on the Bayesian analysis
by using the publicly available EDGES low-band data. We find that the result is
sensitive to the foreground model, the assumption of noise, and the frequency
range used in the analysis. The Bayesian evidence suggests the models with
higher order polynomial function and enhanced noise are preferred. We also
compare models suggested from the EDGES low-band data and the ones from recent
JWST measurements of the galaxy luminosity function at $z=16$. We find that the
model which produces the 21cm absorption line at $z\approx15$ is well
consistent with the central value of the observed luminosity function at
$z=16$.
