  Fast and reliable localization of high-energy transients is crucial for
characterizing the burst properties and guiding the follow-up observations.
Localization based on the relative counts of different detectors has been
widely used for all-sky gamma-ray monitors. There are two major methods for
this counts distribution localization: $\chi^{2}$ minimization method and the
Bayesian method. Here we propose a modified Bayesian method that could take
advantage of both the accuracy of the Bayesian method and the simplicity of the
$\chi^{2}$ method. With comprehensive simulations, we find that our Bayesian
method with Poisson likelihood is generally more applicable for various bursts
than $\chi^{2}$ method, especially for weak bursts. We further proposed a
location-spectrum iteration approach based on the Bayesian inference, which
could alleviate the problems caused by the spectral difference between the
burst and location templates. Our method is very suitable for scenarios with
limited computation resources or time-sensitive applications, such as in-flight
localization software, and low-latency localization for rapid follow-up
observations.
