Currently, we make a distinction samples belonging to background vs signal, and only the signal samples are fed to the GMM. This is suboptimal for samples that have comparable probability of being from background or the signal.
A more natural option is to compute the q_ik for the GMM and the background, and then to renormalize the amplitudes of background and signal. This could reduce some of the code complexity and give better results for the GMM means and covariances.
Currently, we make a distinction samples belonging to background vs signal, and only the signal samples are fed to the GMM. This is suboptimal for samples that have comparable probability of being from background or the signal. A more natural option is to compute the
q_ik
for the GMM and the background, and then to renormalize the amplitudes of background and signal. This could reduce some of the code complexity and give better results for the GMM means and covariances.